I am still thinking of you all and am finding new ways to be in conversation through this channel. Please drop me a note if you’re interested in specific data justice ideas or theories that you’d like me to explore next! Until next time☕

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This has been a generative year for the People’s Data Project! I started out with a concept of data rights education and a gut feeling that the people who I work with in my community could benefit from understanding how systems of power weaponize our data against us, hard-coding oppression into every area of our politicized lives.

When I started talking to my friends/experts about how I should craft lessons or workshops around data rights, I used references from groups like Civic Switchboard who work to connect local libraries to community information networks in cities across the country. Civic Switchboard hosts workshops to help people understand the basics of civic data, for example by helping people create datasets from their own experiences.

Thinking about data creation, I started to wonder, what is it about creating your own dataset that invites you to better understand issues of data justice? What does the process of creating your own dataset actually feel like?

People can feel the difference between data that reflects their own experience and data that reflects someone else’s perspective from above.

A year ago, the NOTICE Coalition, which I wrote about in my previous dispatch, held its first Houston convening for organizers fighting back against surveillance in schools. The gathering started with a Habitat Walk with local community organizer and artist Eddie “Inspire” Garcia who led us through Tony Marron Park on the banks of Buffalo Bayou.

Eddie helped us identify native trees, plants, and creatures and also pointed out the looming golf courses, apartment complexes, and industrial zones on the peripheries of the beautiful views, threatening the housing security and health of the people living in Second Ward.

After the walk, I asked participants to think about what they saw and felt on the walk and to describe it. Some people mentioned the feeling of the wind, the unexpected views of construction on the fenceline of the park, garbage on the ground, milkweed, butterflies, native trees, and boats passing by on the water. We talked about how if we had to describe the park in data, we might try to find ways to capture the wind speeds or air quality, the number of native vs. invasive species present, the registrations of boats or barges passing by on the water.

Then, I asked people to think about how someone viewing the park through a surveillance device might describe it. They might be using cameras, so they’d be more interested in the number of people biking or walking on trails rather than observing the plant life alongside them. They might use sound recording devices so they’d be more interested in the decibel counts or noises from construction rather than the soft flow of the water.

People immediately noticed the disconnect between the park they felt and experienced and the park the way it would be viewed from above. That day planted a seed in my mind, what if people could collect or describe objects they picked up from their own explorations of a place, and then use that to create their own analog dataset about a place in their community. I created an analog dataset of my own to illustrate this concept and planned to use it for my workshop at the Twin Cities Innovation Alliance’s annual convening in July of this year.

In my data bias workshop, I ask participants to create their own data table to capture descriptions of the numbered objects sewn onto my analog dataset. In essence, they are observing objects represented in the physical world and creating a summary dataset to be able to then describe the objects to the group.

Each participant is given their own table and encouraged to work alone to define the fields and determine which unique identifiers will ensure that they can differentiate between the objects in their table later on. Some people give the objects names while other people rely on their associated numbers written onto the cloth, but everyone is free to choose their own system of arranging the data.

The point is exactly in that subtle difference. From the same set of 35 simple objects, five people can create completely unique datasets depending on their perspectives or interests. Participants are sometimes shocked to find exactly how different their perspectives are. One person approached the objects as clutter in her home and classified them by how likely she’d be to throw them away while another categorized the objects by choking hazard, thinking about her toddler at home.

Made real, bias becomes an obvious fact, inherent in any data creation process. And after talking about it for a few minutes, people start to ask the same questions: Why are governments and scientific institutions so quick to tell us that our data is biased (or inadequate or insufficiently rigorous), when it’s so glaringly obvious that theirs must be too? Once we start thinking of “bias” as just a version of “perspective” — recognizing that it is harmless on its own — we can see that unbiased data is ultimately an illusion.

We’ve been taught to feel shame about bias because systems of power benefit from the illusion of “unbiased data.”

When I made this point in front of a room of environmental scientists at the Environmental Data and Mapping Symposium in Houston this past July, I could feel the whole room almost jump out of their seats in a collective, “Yes!”. I wondered later, how many scientists have gone through their whole careers in academia fearing the appearance of bias, trying to minimize it through layers of statistical methods, running from the reality of it at every turn?

Hazel Tohe was on that panel with me. She helped create the Dine’-Centered Research and Evaluation Model and leads efforts to research environmental issues affecting people in Navajo Nation, using Indigenous research methods built on Dine’/Navajo epistemology. For Hazel, presenting data and research with a perspective that exists in opposition to colonial research practices is the entire point. It requires not only an “acknowledgement of bias” as scientists would say, but a loud, affirmative declaration of the Dine’ lens that guides all of her work.

When you have a cultural foundation that allows you to approach research with specific values and goals connected to the livelihood of your community, your bias, which would be described as a an “error” within colonial statistical methods, becomes a perspective that aligns your work to a different compass. Hazel writes, their purpose is “to strive for a life of health, balance and prosperity, and to nurture a consciousness based on our understanding of our relationship to one another and all life.”

So, then, what’s happening on the other side? In the last few years of my time working with state and local governments on data policy and open data programs, I was interested in helping data teams implement bias assessments and stronger metadata practices to ensure that agency data staff were trained in how to acknowledge and document bias in the datasets that they collected and created.

Government case managers, data analysts, and software engineers make dozens of decisions about how to collect the information that’s held in any given dataset. They decide how to word the questions behind the information (think: “were you late on your payments?” vs. “when did you submit your payment?”).

These decisions can have long-term effects. I’ve seen multiple cases where policies change and all of a sudden it doesn’t matter if people are behind on their payments anymore, late fees are forgiven. But the software is only coded to collect data on whether payments are late, not what date the payment was actually made. All of a sudden the data is useless because it was designed for a different political priority.

Consider a corporate example: An employer who plans to punish people for being late to work will only be interested in tracking whether a worker was LATE or NOT LATE, which inserts a bias into the data that sees lateness as a bad thing. Their goal is to punish workers for their mistakes, which is reflected in how the data is collected. So by looking at the metadata — the data about the data, the definitions and questions behind it — we can see the employer’s perspective and their values related to issues of labor rights and capitalism.

Based on this example, you can see why an employer holding this perspective would want to hide it. It’s ugly. They’d want you to believe that their perspective is the only perspective, and they’d say that the data is essential for company performance, no questions asked, no data rights necessary. But if we can start to look for the data around the data, or behind it, we can have more insight into the perspectives of the people who craft the data, and therefore their positionality. And this has been my biggest lesson to-date.

Bias is an entry point to talking about data justice because it exposes positionality.

Most people I talk to about data rights or data justice think I’m talking about forcing organizers to use AI or other mess like that. People aren’t used to thinking about the power dynamics behind the data systems that increasingly govern our lives.

And honestly (very honestly) many of the projects I’ve come across in civic tech or open data world that attempt to work with “communities” don’t seek to grapple with any real world power dynamics in data or technology systems. They stay in the comfortable zone of diversity and inclusion, increasing opportunity for a more diverse range of people to participate in the existing systems of power that surround technology and data. This is an unfortunate manifestation of a de-politicized liberal agenda in spaces often said to be radically futurist or democratizing technology.

Examining the bias behind data systems is the first time most people think about the potential that the data we see or that is circulated about us in data markets is actually extremely biased to reinforce a capitalist, patriarchal, racialized, imperialist worldview.

It’s easy enough for us who work on data in our own communities to name our biases, we can examine our identities and positions and state the intentions behind why we are collecting data to serve our communities. It may not be perfect, but it is honest to where we are and we can acknowledge that data emerges from our own specific positionality and perspectives.

But what if we asked governments, corporations, and academic institutions to do the same thing? What if they had to name the values behind why they are asking for our information and tell us what they plan to do with it? What if they had to be honest about the impacts of their data collection on our lives?

I have a hunch (based strongly on my own experience) that if we start asking these questions, we will start to see power dynamics emerge in real-time. I have asked them before, and the reality is that governments avoid these questions because they can. Local governments are not accountable to communities in how they collect or manage data. There is often no meaningful consent when data is collected, it’s mandatory to provide if you want to receive any range of government services.

Without public pressure on our rights to our data, governments face no incentives to acknowledge that they are only collecting data on SNAP recipients’ incomes so that they can punish people who draw benefits when they are no longer eligible. Even if those families still need food, even when the cost of living has increased and being technically above the eligibility line doesn’t mean you can afford food for your family. The data says you are ineligible. And as they would tell it, the data doesn’t lie.

In this Dispatch, I’ll share a little bit of what I’ve been learning from artists, organizers, friends, and collaborators working against surveillance and for community control over data. Carving new neural pathways, letting emerging lessons shape our work — that’s how we’ll find our way to a just data future.

🎓 Giant puppets tell the story of students under surveillance… At the Twin Cities Innovation Alliance’s Data for Public Good conference this past July, students, artists, academics, and organizers came together to discuss topics like critical data science, issues of criminalization and surveillance in AI, and education technologies in media.

All year, the NOTICE Coalition has led No Data About Us Without Us Fellows through a series of educational workshops and research projects to understand and name the challenges they face related to surveillance in their schools. Artist KillJoy and their mycelium network Kitchen Table Puppets & Press worked with Fellows to build puppets and craft a performance that would shed light on types of surveillance that students face, from vape detection technologies to attendance tracking systems.

The performance followed three puppet-students who face different kinds of profiling and policing because of surveillance used in their schools. One student is sent to the “Learning Center,” a high-surveillance alternative school, after searching for banned information on school computers — a growing problem as the State of Texas enacts book bans and stifles freedom of speech in schools. Other characters spoke to the challenges that students with juvenile correctional backgrounds or undocumented family members face when they’re targeted for disciplinary action by any number of surveillance technologies that schools have at their disposal.

Closing with a passionate spoken word poem presented by teacher and organizer Jeremy Eugene, “Digital Dystopia” helped people feel the injustice that students and teachers experience when schools hold their students down under policies of unlimited surveillance and punitive data systems.

Experiments: Grassroots data collectives

Are you someone who is currently experimenting or has experimented in the past with community owned data infrastructure to support movement organizing or advocacy?

One benefit of being known as the town “data person” in organizing spaces is that I get to talk with people in the very early stages of their questions or ideas about how to use data in their work. As I’ve long suspected, most people don’t need dashboards or visualizations to solve all their problems, at least not until the very end of their projects when it’s time to communicate what they’ve found.

Needs that do arise when I’m talking to people include some that data folks might recognize from across contexts: The need to better understand our own behavior and long-term patterns or trends in the issues that we’re tracking in our communities; the need to have difficult conversations about the guardrails around how we collect and store community data when we plan to use it to advocate in spaces of power.

One day some of these projects might be developed enough to share about in public, but for now I’m interested in hearing from anyone who has built data infrastructure specifically with the intent of community ownership, especially building self-hosted databases; or anyone who has created community data licenses to protect data in partnerships with research and academic institutions. If this is you, email me at katya@peoplesdataproject.org!

💡 Crafting a new lexicon to meet the moment…

In my last Dispatch, I explained how the term “unjust data narratives” captures a deeply felt paradox that people feel when they look at data-driven stories about them that fail to reflect their lived realities.

Further building out the lexicon around data justice, today I’m going to share a few of the concepts guiding my decision to use the term “data extraction” to explain how institutions and industry collude to generate power or profit from collection, analysis, and use of our data.

• “Extraction” as a concept → The phrase “data is the new oil” was originally introduced by proponents of industry in the beginning of the 21st century as an overall positive and exciting concept driving the future of big data and algorithms. Despite the phrase being somewhat worn out and receiving relatively minor critiques from data policy world, it captured an actual sentiment in the private sector that their goal would be to extract as much of this valuable asset from people and communities as humanly possible. As I talk to people in my own community about concepts of data justice, using the word “extraction” is what shifts their mindset from innocent data “collection” toward the reality of actual exploitation of community knowledge.

• “Data Entrapment” as a parallel term → Another angle on data extraction, data entrapment is a term proposed by Marika Pfefferkorn in her article Data Justice and the Risks of Data Sharing to expose how institutions capture and manipulate data for the purposes of enacting their own agendas which may in turn be harmful to youth and communities. Thinking about extraction and entrapment as adjacent also allows us to compare different power dynamics that are at play when data is held by industry or institutions.

• Ontologically treating data as a natural resource → I learned through my previous research on data rights and Indigenous Data Sovereignty that Indigenous forms of knowledge arise from land-based, embodied practices sometimes emerging from data gathered by humans to understand their natural environments. Considering data this way, it makes sense to connect the extraction of data to the extraction of lands, much like how colonial systems seek to control Indigenous lands and as a result also require control over or extraction of Indigenous cultural practices and knowledge. Understanding data as something connected in essence to natural systems helps us realize that its extraction without embodiment in context fundamentally disconnects data from its true purpose.

Wait — I have some questions!

Before I go any further, I would like some input from you, my readers! I want to prioritize making and sharing things that you care about the most. As I travel through places and conversations picking up threads to weave together for this newsletter, I’d like to be guided by the things that really energize you.

Thank you for participating in this community poll! Your insights will help me grow Civic Source to become a trustworthy, energizing part of your work and practice.

🛠️ Seeding workshops & a data rights curriculum… Now on the People’s Data Project website is a write-up of this summer’s data justice workshops and events! From the Twin Cities to Taipei, I’ve been fortunate to collaborate with folks who are applying data justice in new and interesting ways, bringing tools and resources to folks around the world. We talked to environmental justice advocates, researchers, human rights activists, students, teachers, and tech workers, all of whom are seeing issues of data injustice appear throughout their work.

Many thanks to Emelia Williams at the Open Environmental Data Project and Mashal Awais at Third Pole Solutions who helped co-create these workshops with me. Stay current with the People’s Data Project by subscribing for updates through our wesbite.

Where I’ll be next…

• Voqal Fellowship Alumni Reunion, Phoenix, AZ, October 8-12

• Know anyone doing data work in Bogotá or Medellín? I’ll be on a mini-research trip and visiting family in December and would love to connect with new allies!

As an independent researcher and organizer working in grassroots contexts, I am relying on multiple small-scale funding channels to support my work. One way you can support my work is by becoming a paid subscriber on this newsletter! I hope to continue publishing 4-6 well-researched articles on the foundations of data rights per year. You can also submit an inquiry to the People’s Data Project about how data rights can support your organization. Thank you for supporting public research!

Since I started writing about data rights, I’ve been really attuned to noticing the different ways that people describe our rights to our data. Some people talk about “data privacy,” which exists in a policy and legal context and speaks to individual privacy. Others talk about “data security” which implies a technical lockdown and summons ideas of cybersecurity.

Whether it’s either of these or data protection or data governance, all of these popular terms occupy a space where data rights should be. None of them draw attention to the fundamental function of data, or acknowledge its essential role in our lives, which is the role that data plays in connecting us and helping us build understanding with one another.

Data is like water that runs through us and connects us. It’s a medium for communication that at its most basic function helps us communicate our perspectives and build a shared reality. Data, which carries information, knowledge, or even wisdom, exists in the same realm as stories, helping us weave together a human fabric.

I’ve come to see this reframe from data as resource to data as medium as one of the most trippy and paradigm-shifting lessons of my research. Because if we can see community data as the digital representation of our communal well of knowledge, maybe we can also see that exploitation is when corporations and systems of power try to extract the water for goals that don’t serve the community (like privatizing the water or using it to power a nuclear facility).

The more I’ve delved into these ideas, the more I’ve realized that I am far from the first person to arrive here. Indigenous scholars from around the world have been fighting to defend Indigenous peoples’ sovereignty over their cultural knowledge and data by working to define the many ways that Indigenous data is extracted toward harmful ends. It’s only within Indigenous Data Sovereignty that I’ve found expanded definitions of what data can be, and meaningful exploration of the powerful social fabric that communal data maintains.

In this installment, I’d like to share what I’ve learned so far from IDS movements with acknowledgement that Indigenous scholars are first and foremost defining these concepts and that this just an attempt at synthesis as a guest in these spaces. I welcome any additions or corrections to this analysis.

Through the framework of Indigenous Data Sovereignty, tribal communities around the world are fighting to reclaim agency over collection, storage, interpretation, or use of their data, ultimately to protect their people and sovereign lands from the destabilizing forces that threaten all of our global ecological systems. Learning about Indigenous Data Sovereignty has helped me understand the true power of data rights, and I believe that it can reorient all of us to a different way of understanding data’s role in our own communities.

The new paradigm of data protection must center land-based community knowledge.

“Indigenous peoples have always been data warriors. Our ancient traditions recorded and protected information and knowledge through art, carving, song, chants and other practices. Deliberate efforts to expunge these knowledge systems were part and parcel of colonisation, along with state-imposed practices of counting and classifying Indigenous populations. As a result, Indigenous Peoples often encounter severe data deficits when trying to access high-quality, culturally-relevant data to pursue their goals but an abundance of data that reflects and serves government interests regarding Indigenous Peoples and their lands.”

The Indigenous World 2021: Indigenous Data Sovereignty by Stephanie Russo Carroll, Tahu Kukutai, Maggie Walter

In their article in Indigenous World, Carroll, Kukutai, and Walter lay out the foundations of Indigenous Data Sovereignty as well as some organizations and principles guiding the IDS movement today. Their goal is for institutions to not only support Indigenous Data Sovereignty but to also enact it in how they handle data, especially within the private sector.

Indigenous Data Sovereignty emerges naturally as an extension of the sovereignty that Indigenous peoples have over their respective ancestral lands. As Desi Rodriguez-Lonebear writes, Indigenous tribal communities have spent generations developing systems of knowledge and passing down information about how to survive.

“Contrary to colonial narratives of savagery and unsophistication, indigenous peoples were relentlessly empirical with advanced systems of knowledge. For indigenous peoples, data were everywhere, and survival was often tied to one’s ability to gather, analyse and share this knowledge. The winter counts by the Plains Indians are an example of the meticulous and methodological nature of indigenous data. The Lakota, Blackfeet and other Plains tribes recorded winter counts on animal hides to enumerate important aspects of their world. These detailed counts included numbers of tribal citizens, allies, enemies, wild game, lodges and so on: histories and assemblages of data that were instruments of survival.”

Building a Data Revolution by Desi Rodriguez-Lonebear

Rodriguez-Lonebear goes on to explain that their own tribe, the Northern Cheyenne, facing pressure to develop lucrative coal and natural gas deposits on their land, cite an oral history as a data point strongly influencing the tribe’s decision never to extract these deposits. “The Cheyenne prophet Sweet Medicine, one of our most powerful figures, foretold that the Cheyenne would one day encounter a black stone beneath our lands. Sweet Medicine warned that this stone was to be left alone if the Cheyenne were to remain Cheyenne… Cheyenne oral history remains a critical source of data as we grapple with contemporary issues. It directly challenges the idea of data as products of modernity with little relevance to indigenous lived experiences or traditions.”

Indigenous cultural knowledge is passed down through generations of storytellers and culture bearers who maintain complex, highly intentional systems of managing the flow of information to younger generations. When corporations and governments use data to make the case for forced displacement or land use that harms tribal communities, they are claiming a supremacy over data that rejects these communal ways of knowing.

While these “data management” practices are unique to different tribal communities and specific to Indigenous ways of knowing, I believe we can learn from the ways different Indigenous communities treat data, especially to help us repair the global ecological instability that is affecting all of us and grows more dangerous each year. In order to protect data rights and heal our own communities, we need to be in solidarity with movements for Indigenous Data Sovereignty.

The same systems that extract Indigenous data for harmful purposes are extracting data from Black, immigrant, and trans communities to the same ends. We all need data rights.

One of the harmful ways that systems of power often use community data is to create unjust data narratives. These unjust data narratives reinforce stories about our communities that do not reflect lived experience but instead serve to justify policy decisions and systems that fail to address the root causes of the challenges that our communities face.

Palawa sociologist and IDS scholar Maggie Walter has already named this phenomenon the “5D Deficit Narrative,” in the Australian social work context. In her essay “The Voice of Indigenous Data: Beyond Markers of Disadvantage,” she describes how data collection about Aboriginal and Torres Strait Islander people without their meaningful consent or participation has led to reproduction of multiple common deficit narratives.

The five “D’s” of the deficit narrative are disparity, deprivation, disadvantage, dysfunction, and difference. As Walter writes, these deficit narratives identify the Indigenous individual as the “problem element” and go on to inform policies that may negatively affect Indigenous families and communities by attempting to remove these “problems.”

This understanding of deficit narratives may also be a framework for understanding how our governments use data to create narratives that negatively affect Black American families and neighborhoods.

The public school system, the prison system, and the social “safety net” are all systems where Black Americans are overrepresented and highly surveilled; where data that are collected consistently reinforce deficits and lacks instead of representing the goals and narratives that Black communities might identify for themselves in order to better address the root causes of the issues that they face.

When I used to work on government-funded community development programs, I consistently saw policymakers use labels like “at-risk”, “truant”, and “recidivist” to describe people, always in the context of data points that supposedly proved these labels to be true.

Indigenous communities everywhere face the same type of pressure from colonial governments as a result of extraction of their data. At the same time, immigrants are increasingly under the microscope of the current administration which is mining any and all data to perpetuate unjust narratives about criminal affiliation and delinquency. And our state government here in Texas is scouring driver’s license databases to identify and target trans Texans. We are stronger when we recognize the commonalities between these experiences and situate ourselves in the landscape of data extraction.

In addition to supporting Indigenous scholars, we can learn about the CARE principles and best practices to enact them.

Published in 2016, “Indigenous Data Sovereignty: Towards an Agenda” gathered Indigenous voices to illustrate the history and present-day challenges of Indigenous knowledge collection to propose new paths forward. Since then, the Global Indigenous Data Alliance (GIDA) has developed a series of principles that “seek to shift data relationships from regulated consultation to value-based dialogue that forefronts Indigenous cultures and knowledge systems within data ecosystems.”

Convened by Maggie Walter and Desi Rodriguez-Lonebear, GIDA was formed at a workshop bringing together participants from seven nation states including representation from the Maiam nayri Wingara Collective (Australia); Te Mana Raraunga Maori Data Sovereignty Network (Aotearoa New Zealand); and the United States Indigenous Data Sovereignty Network. GIDA’s CARE Principles for Indigenous Data Governance are: Collective benefit, Authority to control, Responsibility, Ethics. You can read more about these on GIDA’s website.

Beyond these global definitions, it’s important to note that different tribal communities might have different practices related to data and knowledge management. This position paper on Indigenous AI developed through a series of workshops in 2019 “illustrates how different visions of how AI might be built according to values articulated in Anishaabe, Coquille, Kanaka Maoli/Blackfoot, and Euskadun epistemologies” and suggests prototypes that might put these unique value systems into practice.

These cultural articulations of value systems applied to data management and knowledge exchange are I think the most radical and liberatory frameworks that exist for taking back control of our data from the people operating powerful systems that are harming the world.

And while my work does center “data rights,” this research has made me feel like I need to double down on the difference between data rights and data sovereignty, because I believe that true data sovereignty belongs to Indigenous peoples.

So how can we play a role? That’s what I hope we’ll figure out together. We can practice protecting our own data rights so that we can be in solidarity with Indigenous communities when they demand theirs. We can explore value systems within our own communities, or ways that our own ancestors may have passed down land-based cultural knowledge that we need to protect. We can start to build community data systems that carry the stories, omens, and lessons that will help us survive and build a new future.

LINKED THINKING

• “Indigenous Peoples Breathing Data Back” - Talk by Dr. Stephanie Russo Carroll for TEDxUArizona on ways to bring “databack” and the power of IDS

• “Indigenous led innovation: Aligning Technology with Community Values” - Episode of the Info Matters podcast with Jeff Ward, CEO of Animikii, an Indigenous-owned technology company

• Traditional Knowledge labels - A product of Local Contexts, an initiative addressing intellectual property issues of Indigenous communities

I want to extend a special thank you to researchers and public intellectuals who publish their work for free with open access. My work would be impossible without you :)

Dispatch from the Data Garden 🍅

A new section of Civic Source dedicated to documenting experiments in data rights education practices. While the main newsletter will continue as a space to theorize about expensive possibilities for data rights as a tool for collective empowerment, this series will be a more grounded space for folks to see behind the curtain and join the work of spreading tools and resources about data rights principles throughout our communities.

🌱 Sowing seeds of new knowledge… The data garden is a metaphorical space grounded in place and in community, where we can start to think of data not as digital matter stored in a cloud far away, but in our soil, cultivated by our hands.

The data garden is a space of growth and possibility, where stories can emerge naturally from people who plant the seeds. The lush harvest of the data garden must be protected at all costs from predators who want to swoop in and extract its precious nutrients and use community data to their own advantage.

Caught me in the wild!

I really enjoyed being a part of Data for Black Lives III held in Miami at the Perez Art Museum last November. I highly recommend checking out the recording of the opening which featured a beautifully grounding performance and incredible invitations from Yeshi Milner and Dr. Fallon Wilson to build community institutions for liberatory futures to endure shifts in sociotechnical landscapes that seek to separate us from our collective purpose to live in alignment with each other, our ancestors, and the natural world.

You can see the rest of the streamed content from D4BLIII on their YouTube page, including my panel with Noni Session, Dr. Michael Akinwumi, Natasha Duarte, and Rasheedah Phillips. Our panel “Reparations AI: Housing” invited participants to speculate about ways data could be used as a transformative tool to restore and invest in Black communities harmed by racial injustice through the housing industry.

📖 Creating a more accessible lexicon for data rights… The way we talk about data is skewed toward people who directly touch datasets or work with data itself. This is a hugely inhibiting force for a majority of the general public to understand how to engage with data politics. The emerging reality of our datafied world is that even people who aren’t comfortable with data will have to be a part of figuring out how it should be used, especially when it comes to data about them and their community!

That’s why I’ve started testing new language to describe the data rights landscape —language that doesn’t revolve as much around technocratic modes of data governance, but that rather describes the sociopolitical structures around how data is extracted and used (or abused).

• Unjust data narratives: The fight for data is fundamentally a fight to control narratives — narratives that are used by people in positions of power to make decisions or generate profit. Unjust data narratives are the primary product of data extraction practices that reinforce the power of harmful systems.
  
  Why this language works:

  • Academics call the fight for data rights an issue of “control over access to personal information,” which is both a mouthful and is rather abstract. Drawing people’s attention to the stories that are created about them when “bad people” are in control can help make the injustices of data extractivism into something real and show the importance of seeking collective control.

  • Using the language of stories helps to connect data injustices to a core element of the human experience — our soul connection to stories. Stories are core to how humans understand diverse perspectives and are a key part of how we learn. Connecting data rights to the inherent right we have to tell our own stories shows why data rights issues are universally important.

  • Many organizers and advocates are already familiar with the need to control narratives in culture and media in order to fight back against injustice. Harmful narratives in the media not only hurt individuals, but can be weaponized against entire groups of people. Talking about unjust data narratives allows us to think about how data extractivism not only leads to violations of personal data privacy rights but can also be weaponized against entire groups.

It’s important that language about data rights evolves beyond policy and academia. We need new terms that are broad and accessible to allow more people to participate in conversations about how data is being used by powerful people and institutions.

🤔What do you think about unjust data narratives as a term to include in conversations about data rights? Is there another way you’d frame the issue?🤔

Experiment: Rights-based data strategies for organizations

Last summer, I started working with Bayou City Waterkeeper (BCWK), a Houston-based organization fighting for water quality, wetlands protection, and flood mitigation through legal advocacy and community organizing. Our goal was to help the organization move towards implementing a rights-based data strategy.

In the past, I’ve been interested in grassroots collective data practices like data cooperatives or community data trusts, but after talking to people in my own community who would be the best candidates to participate in such projects, I realized that many organizations have very little capacity or resources to effectively work with their own data, let alone to share with others.

• Lesson: Setting up practices to get good at sharing data within your organization makes it much easier to start sharing data strategically with external partners. Using data in just and equitable ways means using it to start conversations between staff that increase understanding, communication, and collaboration.

The project to help BCWK with a rights-based data strategy has also revolved around helping the organization address a fundamental need to integrate data more deeply into the wide range of advocacy and organizing work that they do. For many community-serving organizations, this can be a daunting task because introducing data as a tool can sometimes be alienating to non-technical staff or to community members, creating perceptions of hierarchies of knowledge that make it harder for people to work together.

• Lesson: Orienting internal data practices around data rights helps to ground technical teams in practices that are inclusive and accessible to people who are represented in the data. Doing so allows non-technical staff and community members to have a meaningful role in deciding how data should be used.

Community members need to be a part of determining how data is used by organizations that represent them, and been also begin using that data themselves to become their own advocates. With that in mind, community organizations often need to play an intermediary role by processing data that comes in from governments or other institutional sources, and sharing data in accessible ways that meet the requests or needs of the people they serve.

• Lesson: A rights-based data strategy should create space for people interacting with different audiences to unpack how the data needs of their audience differ. This way teams can also communicate and work together to ensure data rights principles are being met in practice.

The work continues… These are preliminary insights from research and conversations conducted with my main collaborator and co-conspirator Mashal Awais, Community Science Manager at BCWK. Mashal is a trained environmental scientist and GIS specialist interested in deconstructing traditional methods of data use in community contexts. People like her are the reason this work matters! We'll continue to share insights and resources as this work develops.

Reach out for more information about resources and tools to support rights-based data strategy work for grassroots or movement organizations.

✨ Inspiring work in science and technology education… Free Radicals is an activist collective dedicated to creating a more socially just, equitable, and accountable science. They create accessible resources for political education on the intersection of science and social justice and offer collaborations with politically aligned organizations on issues related to science and technology.

Their zines are vibrant and engaging, using art to illustrate topics like Walmart’s use of technology to exploit workers, Seuss-style systems of data extraction, and LAPD’s abuses of data for predictive policing.

I found out about Free Radicals’ work through Sophie Wang who also created a zine for the NOTICE Coalition called Jamal’s Journey. The zine is a walk-through of 22 real-life ways that student data is collected, shared, sold, or stored, and what some of the issues and impacts are that surround this surveillance. Explore more of Sophie’s work or purchase the zine.

Art has an essential role in translating difficult concepts like data rights into easily understandable themes. Because technical systems are often invisible and abstract, I like depicting them through natural analogs (like flowers and tomatoes!) to help people ground their understanding in embodied contexts. Watch this space for more artistic interventions in popular education around data rights!

Where I’ll be next…

• RightsCon Taipei 2025. Catch me at these talks or panels in-person!

  • Tuesday, February 25 at 9am: “Data has a beating heart: situating environmental data and rights in embodied landscapes”
    Workshop hosted by myself and Emelia Williams in collaboration with the Open Environmental Data Project.

  • Tuesday, February 25 at 11:30am: “Navigating GeoAI for climate action: potential and pitfalls”

    Group dialogue with Dona Mathew, Digital Futures Lab; Pyrou Chung, Open Development Initiative; and Ruth Schmidt, FAIR Forward on AI’s uses for climate action and ways to empower climate organizers through emerging data practices.

  • Wednesday, February 26 at 12:45pm: “Data rights, data protection, and protection from dangerous data”

    Speaker showcase where I’ll be giving a lightning talk on lessons and strategies for liberatory local data futures!

• National Technology Education Network 2025 Conference. Presenting on data rights tools and resources for nonprofit organizations. Details TBA.

See you out in the big bad world! 🌎 What’d you think of this dispatch? Let me know or drop me an email if there’s something else you’d like to see.

For years, scholars, artists, and public intellectuals have been trying to show us that the tech industry manipulates society and builds power by dominating the attention economy, which keeps the general public distracted and young people hooked on algorithms that fuel depression and anxiety. They do this so that their CEOs can continue to amass personal wealth, widening the already huge gaps in income inequality across industries.

Tech leaders have also gained massive global influence through the exploitation of surveillance capitalism. They’ve ensured that a majority of industries are now dependent on personal data, collected through large-scale media platforms used as tools of surveillance. They sell our data through a ballooning advertising industry with global reach that in turn serves tech leaders’ interests.

I’ve trapped my friends in enough rants in enough living rooms. I know that thinking about these structures tends to leave people overwhelmed with a headache. Perceiving the hugeness without understanding what tools would help us undermine this system paralyzes us out of taking action.

It’s a fair question: If Google, Meta, X, Amazon and other tech giants wield such tremendous corporate power that they can buy out any company and swing any election, how can we have any power ourselves?

The answer is in the culture that the tech industry has built around itself. A culture of complete and utter reliance on data that is extracted through fundamentally unethical means.

Across industries and sectors, not just in tech and not just in the private sector, companies and governments have shifted their entire organizational strategies toward “data dominance”. This term appears in consulting decks and business magazines across the internet; it has been woven into the fabric of how corporate America operates.

Data dominance is actually a military term that describes “the ability to swiftly acquire, process, and act on information.” According to the World Economic Forum, "Dominant firms are those who have the most data that can be deployed behind the most profitable business models."

We can see it manifest differently in different contexts. Real estate developers can wield data dominance to make strategic investments that will lead to the gentrification and displacement of entire communities, and health insurance providers use black box algorithms to more efficiently and secretively deny healthcare claims.

In addition to fueling strategic decisions across sectors, data extraction is also a significant chunk of every major tech platforms’ overall revenue and therefore a foundational part of their business models. About 98% of Meta’s revenue in 2023 came from advertising. The ads effectively sell products because they target consumers using their own personal data — personal data that was collected without meaningful consent and with the intention of generating profit.

The tech lobby is extremely active against any kind of legislation that would lessen corporate power to extract and abuse unlimited amounts of data. They are specifically interested in avoiding and blocking conversations about data rights in any sphere because unrestricted access to information is the source of their power.

Governments have been knowingly or unknowingly complicit in the tech industry’s agenda. All levels of US government have been influenced by decades of conservative and moderate administrative reforms, which say that the government should run like a business.

Government administration fundamentally runs on data (or forms if you prefer to think of it that way). So their methods of data collection directly impact the governing decisions that leaders make. But when many governments made the transition from paper forms to digital, they were heavily influenced by the data collection standards of the tech and consulting industries, which have been responsible for building most government software.

It’s natural then that government leaders think they need tech leaders on their side, taking the corporate way to most tech solutions, embracing public-private partnerships, and avoiding investment in truly community-owned or cooperative public solutions. As a result, governments also avoid and block conversations about data rights because they don’t want to overhaul their data collection practices, and they don’t prioritize legislation that would protect people from systemic abuses of data. (The feds have mostly legislated to make government data use more efficient.)

While the US has some legal protections around health information and student information, these protections are extremely basic. Without more meaningful protections, the extensive government data systems built to manage everything from food stamps to immigration asylum requests to school attendance are easily weaponized for surveillance or policing depending on the goals of different administrations.

Data is a tool that governments are happy to help law enforcement agencies and the criminal legal system use to track people who are receiving public benefits for so-called fraud prevention purposes, or to track and separate families through the “child welfare system.” Data is also a fundamental tool in the dehumanization process when governments hold people in jails and prisons, reducing human beings to cases and numbers.

If governments really cared to protect people from abuses of their data, wouldn’t they have put some legal barriers in place to prevent these and other harmful uses? Like so many other issues of our world, the answer is left in the peoples’ hands.

If we want to stop abuses of data enabled by the broad reach of the tech industry, we have to look more closely at the source of their power. The source of their power is us.

Seeing how many companies and institutions strive for data dominance, we might also start to see the power we have at the bottom of the terrible food chain. Unfortunately tech leaders have proven you can get rich and famous on the back of systemic abuses of data while many people don’t even realize what is happening. This freedom to exploit had enabled the data rush around AI and increased the urgency for many companies to achieve data dominance. But we are the “data subjects” that power the data systems that fuel these industries.

Refusing data extraction would be equivalent to turning off the tap. Extractive data practices that reinforce corporate data dominance are so widespread that we’re faced with probably dozens of opportunities every day to refuse in ways that could help us start to build power.

The challenge is that because these companies and their data capacities are so huge, individual action isn‘t enough. Of course it still helps to do things like reading terms and conditions, turning off your location data, or declining facial recognition at the airport. But we have to get better at identifying the opportunities for collective intervention in data extraction.

This is where I stop having answers. I’m always looking to my friends and peers to help ideate about what collective mobilization against data extraction could look like. That’s the exciting work that lies ahead for us. We could dream up data strikes or data unions. Community organizations that we trust could help form the first lines of community data defense.

We need more and different ways to inform people about the channels of data extraction that are touching them in their own lives. Like other forms of collective bargaining, this could mean helping people come together to negotiate for dividends of the profits from data sales or to demand changes they want to see in systems of power.

Because data dominance as a practice is already so widespread, there are countless industries that would be hamstrung by probably even minimal collective action against data extraction. Payday lenders might have to stop using personal data to decide where to set up shop. For-profit universities might have to stop using it to target susceptible students with false claims about their schools. An overall cooling cultural shift away from data extraction would also help make the big social media platforms obsolete, since data collection is almost their sole purpose.

So — see! There are options. But first, we have to do the work of understanding and identifying how companies are flexing their data dominance in our lives.

We have to understand that our data has value and is worth protecting. It is a product of us and our communities. Companies may weaponize our data to build more efficient means of extracting labor and capital, but the reverse is also true — that without our participation, these data systems would be nothing. They'd wither away in the wind. Something to consider the next time you're asked to consent.

I will continue aiming to produce 6-8 high quality posts on data rights issues per year. Your resourcing will help me approach this work with ample time and intention, so that I can continue offering reflections that I hope will activate and inspire your work! If you’re able to become a paid subscriber at the co-conspirator level, you will also receive a personalized collage art postcard! Thanks to those who have stuck with me so long 🌹

In the recent election, we witnessed a mass refusal of the status quo. Masses of people who are committed to the rights of all people to live full, dignified lives free of colonial violence, genocide, and exploitation refused to support a candidate who could not say the same. 

Refusal matters. So many national nonprofits that I’ve worked with on data and technology policy have good values on paper: to work toward everyone’s well-being, to bring about more equity or transparency. But unless refusal is part of our value systems, we miss the transformative moments that actually allow us to upend bad practices and replace bad processes with better ones. 

Over and over again technocrats look for new words to describe practices that are supposed to invite “community members” into design and bring about general well-being (think: co-design, co-creation, building with and not for). Yet, after using one of these words for a while, we have to move on and find new ones because the thing that’s supposed to happen doesn’t happen. At best, there is marginal input from community participants but it still doesn’t feel right. 

Why is this? I’ve been in countless workshops and design sessions where people throw their hands up to the sky, like “What magic words do we need to say to get these people to feel they have an equal say?” All the while, obvious relationships of power are in the room with us, ones that could only get exposed by raising the issue — raising a challenge — to the system of power itself. 

According to research by Chelsea Barabas in her paper “Refusal in data ethics: Re-imagining the code beneath the code of computation in the carceral state,” refusal as a practice can be considered a form of resistance or a practice of re-centering the margins. Re-centering the margins means stepping out of the frame of endless momentum and inevitability, and instead stopping to look around at the scenery, taking into account perspectives that may have been discarded by the roadside.

As long as I’ve been designing civic processes in community, I’ve held the principle that the person who holds power in a project is the one who can stop it from going forward.  

Many collaborations between institutions and communities that I’ve been a part of have involved  “co-design” so that governments could be more sure that what they decide to build would actually be beneficial to and needed by communities. I was a supporter and designer of many of these processes starting early in my career, before I understood what real community power could look like, but I observed and learned from those experiences. 

It’s possible that being in so many largely fruitless co-design processes and watching them fail to “empower communities” time after time after time was really what radicalized me. In many of these processes, I saw “community members” saying no. I saw them repeatedly, persistently rejecting the work, and I saw governments and their allies going ahead regardless.

In a lot of these cases, the ways that people refused our proposed processes were… I’ll say, creative. Sometimes people would repeatedly skip meetings; sometimes they would lie and go behind our backs to try to stall the work; sometimes they would start outright competing projects; sometimes organizational partners would invite community stakeholders to voice things that they felt they couldn’t say; and sometimes they would outright raise the issue of power dynamics and bias negatively shaping the design process and hope for a legitimizing response. 

In all the times I have seen people through their words and through their actions refuse working with a local government entity, I have never seen a local government actually halt their data or technology project. I have never seen a local government admit, “Maybe this wasn’t the right direction. We’re going to go back to the drawing board and do something different.” 

My takeaway from this was that governments and institutions are not actually interested in co-design or anything like it — that’s why none of the language works. Real co-design requires sharing power. What governments really want is so-called constructive input, feedback, or (in their ideal world) total validation of their efforts with just a few cosmetic changes! 

But I am here to say: If a project doesn’t stop when the community doesn’t want it, then the community does not hold actual power. 

The key to unmasking this blind spot is to understand why we experience so much fear and avoidance around refusal. For each of us, this internalized bias might look different. 

For some, it might feel counterproductive or antagonistic for people to simply refuse what you see as an innovative opportunity. To those I would ask: Why is productivity your main concern if the goal of your work is supposedly to make your community better? 

For others, it might feel scary to start from scratch, to let go of months and years of effort to abandon something and move in a new direction. To those I would ask: Have you had other experiences where letting go of a bad thing allowed you to find something ten times better? Why would this be different? 

Designing for refusal can create the pathways that the people you serve might need to actually express themselves. Creating space for a Hard No can be the fertile ground that allows new possibilities to spring up in its place. It’s by touching in with the people who refuse that we’ll find the answers that we’re looking for, both in design and I think in life.

Designing for refusal is the most powerful tool for social change that is also consistently the least prioritized in civic design processes. 

Functionality for refusal in data and technology systems mostly looks like opt-out functions and opportunities for meaningful consent. But many times, even these basic functions are hidden away from people, making it difficult to exercise these rights. 

In a scan of projects that apply anti-extractive methods of data governance conducted by the Mozilla Foundation’s Data Futures Lab, only about 19 out of 149 have projects were found to provide “Easier management of data about own person/community/group” as the primary benefit, which would include being able to delete or remove information. 

That’s not to say that others don’t provide this benefit but that actual direct control over personal/community information in data systems is rarely the top priority. Often when competing priorities emerge, people make choices that expose their true values. 

And even if consent and ability to opt-out are functions that are desirable under an alternative data governance paradigm, failing to prioritize these functions in design processes shows that people don’t understand their transformative potential. 

It’s through this quiet acquiescence and preferential bias toward moving forward despite, instead of making intentional choices because, that implicitly replicates the same power dynamics that all of us seem to be now talking about upending. 

One of the most impactful and life-altering principles I’ve come to appreciate through dedicated study of abolitionist frameworks is that in order for new possibilities to be born, some things need to die. This concept comes from a deeply spiritual place but also directly from the natural world all around us. 

In order for a forest to experience new growth, some old growth needs to burn. In order for gardens to thrive, they need to be fertilized with compost, dead-alive material. Our current earth systems, dominated by capitalism, are trapped in a life/death paradigm instead of making way for life/death/life cycles which, at the point of death, urge us to trust in the coming of something new. 

We refuse to accept refusal, we refuse to accept the Hard No, because it frightens us from a place deep within our bodies that I believe is situated right next to our fear of dying. 

Long-term, I hope that we don’t always have to be in this place of outright refusal. I believe that mutual trust can be a starting point for the more collaborative forms of design and participation that we hope to see in the world. But we have to acknowledge that we, at least in the US, are not currently at that point. 

“No” is the last refuge of someone who has been pushed out of places of systemic power. It is a starting point for new forms of collective power.

I’m speaking now to people who are actively refusing abusive and oppressive systems of power. Your No is valid. Your No is needed.

Indigenous data sovereignty paradigms rely on a right to refusal to protect Indigenous people from abuses of data fueled by ongoing systems of colonial extraction, as described in this paper by numerous authors called “Indigenous Peoples' Rights in Data: a contribution toward Indigenous Research Sovereignty.” This resource also outlines Indigenous Peoples’ fundamental rights to use data for self-determination, for reclamation of cultural identities, for jurisdictional control over data flows, for data uses toward collective good, and toward digital autonomy and consent. 

For more information on how to incorporate refusal into design of data-driven systems, you can also refer to Jonathan Zong and J. Nathan Matias’ paper “Data Refusal from Below: A Framework for Understanding.” This resource can help answer difficult questions that will arise when you start trying to incorporate refusal into the design of your data systems, specifically outlining data constituents’ common experiences around autonomy, time, power, and cost. 

I share these resources not only because I want to acknowledge the work that has already been done on this subject, but because I want to show that there are already people trying to guide us in the direction of making these observations about the politics of refusal into something real and transformative. 

Implementing refusal as a practice, especially as a designer or decision-maker of data-driven systems, is the first step out of the dark abyss of not understanding, of repeating mistakes over and over again. And as a data constituent, organizing mass refusal is a lever toward systemic change. 

As you begin to observe where refusal shows up in your life or your work, and as you start to respond differently to it, I hope that you will also share your observations. I hope that we will together start to learn how leaning into someone else’s refusal can help us move to different sides of our issues, to find new pathways forward.

All of us can also start practicing refusal when we encounter people, practices, or systems that don’t align with liberatory values. We just have to! Adelante!

LINKED THINKING 

• “No” by Sara Ahmed, Feminist Killjoys 

• “No” by Nicolas Jaar, Sirens

• “To Build a Better Future, Designers Need to Start Saying ‘No’” by Chelsea Barabas, OneZero

I want to extend a special thank you to researchers and public intellectuals who publish their work for free with open access. My work would be impossible without you :) 

I’ve also talked about how the current power dynamics affecting the politics of data favor the right to privacy for people with money and influence, and surveillance by default for everyone else. 

Understanding these dynamics sets the stage for us to understand that a framework for data rights has to flip the paradigm so that transparency flows in the direction of systemic power. 

But what would it look like to actually flip that paradigm? What would the world look like if the value and validity of data was defined from below? 

Obviously, we’re still figuring that out. Western technologies that have enabled mass data collection and surveillance have only existed under the hegemonic rule of capitalism, white supremacy, and principles of individual gain over collective well-being. 

But there are other ways of knowing that undermine these systems. Projects like Our Data Bodies have brought attention to the need to embodied ways of knowing that fight back against top-down data extraction and surveillance. And various other data justice hubs and collectives are cropping up in response to global advancement of AI and other invasive technologies to instead embrace dignity, autonomy, and power in response to data exploitation. 

Many data justice experiments at some point touch on the same foundational sources of knowledge — our bodies and our immediate environments. Our body is our first source of contextualized knowledge, so it makes sense to center our earthly form as we start to explore what bottom-up data justice paradigms can look like. 

According to research by Jennifer Frank Tantia in The Art and Science of Embodied Research Design, we need better language to capture the complexity of our embodied experiences. Jennifer’s research defines “embodied data” as being related to how we describe our bodies’ movements and experiences. In other words, embodied data can be something that emanates from us and that we try to capture through various words and measures. 

If embodied data can be so personal, then why does data so often make us feel like insignificant dots? 

A major criticism of AI is that it harvests information indiscriminately. On an embodied level, it just feels gross to know that every email you’ve sent and every search you’ve made has created a machine that isn’t accountable and has no relation to you. It matters that those experiences are now totally disembodied from the representations of your data as shown through AI.  

The “ick” you feel with seeing yourself in data can happen on any level. Imagine you’re at a community event where data is being shown about drainage repairs in your neighborhood that will prevent flooding from climate disasters. 

You see a chart on the wall about flooding in different parts of your city. You look to find your street, to see if the flooding you experienced last year is represented in the data. It says flooding was minimal. But you called 311 more than five times trying to report blocked drains all down your street! Your neighbor had water up to their doorstep! Sure, it didn’t technically count as more than a foot of water but the blocked drains made all the difference.

What defines that experience for you? How does it feel in your body? How does it feel to see yourself represented in the map? Does it feel bad? 

Now imagine it feels good. Imagine that when you see the map, you’re like, “Yes, this is exactly what I’ve been saying!” Imagine you feel happy that policymakers and decisionmakers will see this information because maybe it means they’ll finally install better drainage on your street. 

Ask yourself, in the scenario where you feel good about what’s represented, which data is added to the account? Is there more context about what data might be missing? Is there space where you can write in additional parts of the story in person? Are there other layers of information that you can add as a viewer of the map? 

Whether this information is included or not is decided by the person who created the dataset or visualization. That data carries their bias. If this data is held up as truth, but the person who collected the data doesn’t share your point of view, it could change how you yourself feel about the representation. 

That’s important to understand because anyone who has ever used statistics is biased by major systemic influences. There is bias that’s built into the fundamentals of how we collect or analyze data in the first place. 

Statistical analysis was invented and championed by eugenicists who measured and compared humans in order to construct scientific standards for white racial superiority. 

The development of statistics, like any advancement in technology, happened in fits and starts across hundreds of years and likely hundreds of scholars. Al-Kindi, a Muslim mathematician in the Abbasid caliphate is the first documented person to study statistical frequencies while deciphering cryptographic messages in the 9th century. 

But in the West, many traditions of statistical analysis were developed by a cadre of European scientists in the 19th century. At the start of the 1800s, the British Empire had already existed for about 200 years with colonies in Africa, South Asia, and the Pacific. By the middle of the century, eugenicists like Adolphe Quetelet, Francis Galton, Karl Pearson, and Ronald Fisher, were constructing some of the fundamental building blocks of statistics today. 

Galton, the so-called “father of eugenics,” coined the phrase “nature vs. nurture.” He felt that successful people got their traits only by nature, and he went about using statistics to prove that. He would work with Pearson and Fisher to invent the concept of “statistical significance,” which is still the standard we use to decide if data is rigorous enough to be considered accurate today. 

Galton, Pearson, and Fisher looked up to a Belgian man named Adolphe Quetelet whose core motivation was to find the ideal “average man” by measuring and comparing human bodies by height and chest size, for example. Because statistically Quetelet sought out “statistical modalities” in the data, or common trends so that he could smooth out the data and find an ideal average, he decided that only bodies of “like races” could be meaningfully compared to each other. As a result, his research centered around building up the ideal standards of the white body, and in the process he contributed to the foundations of how we currently use demography and statistics to compare and average populations.  

This research arrived on the back of previous abuses of scientific research in the British empire including the dehumanizing treatment and posthumous violation of the body of Sarah Baartman. Across the board, developments in statistics in the West came from men who believed in the superiority of the white race by birth, and they created the rules we still use to decide which data are considered valid today to reinforce that belief. 

In order to unpack and disentangle statistics from eugenics, we need to understand what the legacy is actually doing to our ways of knowing, seeing, and understanding. These eugenicists viewed those who they saw as “other” as if from above. They sought to flatten, smooth, average, and extract a single source of truth from the “data” that was people’s bodies. 

That vantage point, from above, still characterizes the way that people in academia, government, and especially the corporate world are taught to see and know things. It also encourages us to use data to seek a certain type of truth, which we could also call an ideal, the same way that Quetelet sought an ideal and an average for the human body. 

Because of the proliferation of racial capitalism through colonialism and empire, these ways of knowing have now proliferated. Business practices around the world that engage with western capitalism have to also engage with its practices of key performance indicators, quarterly metrics, real-time insights, predictive algorithms and the rest. 

These tools are an evolution of the original intent of statistical thought. They exist to flatten and synthesize information for those at the top of the food chain. Embracing embodied data means unpacking and resisting all of that, something too huge to capture in one newsletter. But I believe a starting point can involve allowing for knowledge to be more fuzzy, viewing it more as a tool for mutual understanding than as a single source of truth. 

Data is not the truth, data is a medium for communication. 

If data when viewed from above is understood as truth, reality, evidence, or outcomes, then data when viewed from the ground can be understood simply as conversation or an exchange of perspectives. 

When viewed this way, it becomes a lot less scary that all data is biased. Of course it is! It’s our perspective. And we should be able to put it out there with the right context, the same way we do with our ideas in conversations.  

When someone tells a story that catches you by surprise, sometimes you need to ask for more detail. If you don’t understand what’s being shared or it doesn’t align with your worldview, you might want to ask, “When did this happen? Who was involved? How did you find out??” This is the same as the work that needs to happen with contextualizing data. 

Because even when there is a true version of events to a story, there can always be different perspectives. And it’s possible that all of the perspectives are valid because they are different interpretations of the same actual events. 

You’re not coming in to extract the story and get out. You’re comparing people’s perspectives because you want to actually understand what’s going on, not just logging the event itself, but also getting to know the people. And isn’t that the point of everything? Isn’t that life?

So in order to get to a real truth, you not only need to collect details from one person to understand a dataset, but from many, to understand their various contexts and the ways they understand the story of the data. Only then can you start to understand who is in the room, or who is in the data. The dataset itself is just the starting point for the conversation. 

This has real-world implications. If we favor statistical ways of knowing over embodied ways of knowing, we will miss really important stories that are out there in the world. 

In Houston, people living Kashmere Gardens have known for decades that the Union Pacific Railroad site has caused fatal health effects and toxic contamination in the soil. One person who grew up in the area told me that in her lifetime, she watched the frogs that used to swim in the drainage ditches in front of her house slowly disappear because of the contamination of the water, and now there were no more that she could see. But the area was only validated by institutions as a “cancer cluster” in 2019. Only when these results were “validated” did government agencies and others start to take more significant action. 

Why don’t we believe a person’s decades of observation and insight? Why have we internalized the idea that these other statistical ways of knowing are more important to take as truth? We’ve all done it. It’s a core belief that’s instilled in us through our systems of education and research.

Data as communication might mean that no single perspective is the ultimate truth, but it also means that all perspectives are valid; even with incomplete data, even with insufficient tools to conduct analysis. All perspectives are valid. That is the lesson of embodied data, data from below. 

What would it look like to actually trust people’s stories of their movements and experience —their ways to summarize them and look at them through a new lens. How enchanting is it when you get to see that happen? To see reality through someone else’s eyes.

All Real Radio is a local radio station founded in 2014 and based here in Houston’s Third Ward. Sam’s show reports on the movements that fight back across the Gulf Coast region. You can find more of her work on Instagram and Twitter @samthemullet. Thanks & enjoy 💜

When I worked with city governments launching open data programs with the Sunlight Foundation, a question we’d get all the time was: How do we know what should be private and what should be transparent? What’s the right balance? Basically, they didn’t want to let the “wrong” stuff loose. 

I had an answer down but it was only ever a half-answer: “If you release something wrong or harmful and someone lets you know, you can consider that a favor. Better to know your data’s flaws than to use it without knowing.”

I knew that a principle I believed in was that governments should be transparent. But privacy is also important when it comes to protecting the information of people represented in the data.

I’ve noticed in general that people want to pit privacy and transparency against each other, like opposites, when really they are two sides of the same coin, or two modes on the same spectrum. 

Where we draw the line between privacy and transparency when it comes to sharing our data is an incredibly important question. For example: You might believe that the line should be drawn around you as an individual — that everyone should have the right to total privacy, even if it means shutting out community, government, or any other outside prying eyes. On the other hand, you might believe in radical transparency — that sharing everything with everyone could bring about more communal understanding and togetherness. 

Obviously reality is and will always be somewhere in the middle. But often we’re not the ones deciding for ourselves (if we were, that might constitute having data rights). People with institutional and corporate power are often the ones drawing the lines around who gets to protect themselves from surveillance and who has to share. 

Powerful people have already decided who deserves privacy, and it’s themselves.

In many of the conversations, roundtables, webinars, and panels that I’ve been to about the potential risks of surveillance from emerging technologies, people regularly point out that poor people or people with marginalized identities are the most at risk because they are the most surveilled by public programs, systems of policing, or consumer technologies. 

There was one conversation in 2018 with a group of government policy and privacy experts at NYU to discuss a sort of safety label called the Trustable Technology Mark that would work like a Fair Trade or Organic label to let consumers know if technologies were safe. 

The conversation ended on a pessimistic note. After lots of back and forth about the potential uses, benefits, or risks of the mark, one point of consensus in the room was that the trustmark might not actually solve the surveillance problem. 

Looking at the Fair Trade mark, people pointed out that often wealthy people could afford Fair Trade goods and poor people couldn’t. In the same vein, we thought rich neighborhoods or households would end up with “trustable technologies” and everyone else wouldn’t. 

I remember thinking this was like a far-off reality, that if things got worse, the world could become that way: privacy for the rich, surveillance for everyone else.

And then, last week, I was talking to my friend Sam about Taylor Swift’s private jet (icymi; there’s a Twitter account posting Swift’s PJ flights to raise awareness about celebrity and billionaire carbon emissions) and she told me that in response to backlash, Congress quietly passed legislation in May allowing PJ owners to be listed anonymously in public datasets, making TayTay’s PJ almost impossible to track. 

But that’s not all! There is a ton we don’t know about the corporate world where billionaires and other power players use their wealth or influence to buy privacy. One of my favorite creators on TikTok uses the handle CancelThisClothingCompany to systematically analyze ownership of every product on grocery store shelves. 

Using a combination of public information and inference, he makes “ownership sheets” which he posts on his website. I like that he shows us how easily private equity and corporate power players can hide their outsized influence on our day-to-day lives. Ironically, the slogan on his t-shirts is almost the same one we had on my team at Sunlight: “Information is Power”.

What I’ve realized is that the thing we had all feared in that 2018 workshop is already happening, and was already happening then. It’s not just scary because there’s so much we don’t know about the corporate world. It’s scary because the same people who are hiding behind their right to privacy are the same people building surveillance systems that demand transparency from everyone else. 

We can’t talk about the future of data rights without addressing the surveillance status quo. 

In a 2018 research paper called “The Surveillance Gap: The Harms of Extreme Privacy and Data Marginalization”, authors Michele Gilman and Rebecca Green outline the ways in which people who aren’t surveilled by public systems can miss out on essential life-sustaining public services and are essentially considered non-entities by institutions. 

The term in the title, “the surveillance gap,” is a backwards term to describe the gaps in government surveillance systems, suggesting that a lack of complete surveillance is the reason why people fall through the cracks.

The paper points out that this is a flawed term and suggests that at-risk groups like houseless people, undocumented people, or day laborers deserve dignified pathways to being registered in public systems that build privacy rights. But as we’ve seen, privacy rights already exist, they just exist for the wrong people.

The idea that people have to “come into the light” to receive services is a coercive logic, in alignment with reasoning that the rich and powerful use to ensure that they have the right to see everyone and everything while staying hidden themselves. 

In my opinion, “we can only serve you if we see you” creates a sad excuse for a public safety net. I believe good food, secure shelter, and clean water should be available to everyone without condition. 

That mindset is part of the harmful neoliberal bargain that advocates for the existence of surveillance through justifiable means instead of embracing principles of data rights or ideas like the right to opacity, and working to build consentful technologies from there. 

The right to opacity was a term introduced to me by Yeshi Milner at Data for Black Lives whose work on data justice is helpfully put in context in this chapter from Elizabeth Rodrigues’s “Collecting Lives.” It’s a term originating from Edouard Glissant who wrote:

“If we examine the process of ‘understanding’ people and ideas from the perspective of Western thought, we discover that its basis is the requirement for transparency. In order to understand and thus accept you, I have to measure your solidity with the ideal scale providing me with the grounds to make comparisons and, perhaps, judgments. I have to reduce.” (Poetics of Relation, 189-194)

He says essentially that the right to opacity preserves our right to difference, to differentiate ourselves, to preserve diversity, and to allow our differences to intersect each other rather than assimilating into one dominant paradigm. Data for Black Lives and other thinkers in data justice like Ruha Benjamin use similar concepts to advocate for not just a right to privacy but a right to dignity and embodied self outside of data systems shaped by systems of oppression. 

These are not only rights that we should protect for people who live at the intersection of systems of oppression, these are also rights that the people on top have already claimed for themselves.

Transparency should flow in the direction of power.

I have to credit this quote to Stephen Larrick, who was my first teacher at Sunlight and who helped me get started in opengov world. The idea has really stayed with me because it’s relevant across so many contexts. (Thanks, Stephen.) And because in our world today, the rule is flipped.

It’s actually a simple distillation of how transparency and privacy can work together. People and institutions with more power should be more transparent. People without power have a right to defend themselves from being reduced even further by forced transparency.

As power dynamics shift, transparency and privacy can also shift. That’s why we need data rights, to upend this imbalance.

The shift toward power-informed data rights and a shift away from the status quo may be uncomfortable for some people, especially those in positions of influence or power who feel entitled to the data of working people.

Under the new paradigm, communities may choose to share data with each other as a form of power-sharing and trust. But institutions that don’t share power like governments or universities may be left out. 

This opens up new possibilities for people who want to share data with each other as part of their work to build just and liberated communities. My rule of thumb: Transparency for people who share power with me, opacity for everyone else. 

You might remember that in 2021, I wrote a series of articles based on my own research and conversations with people in government and civic tech about the unseen ways that data and technology systems shape our neighborhoods and communities. 

Bringing light to what data and technology have to do with our everyday lived realities — our sidewalks, our parks, our libraries, our friends, our jobs — was my way of trying to bring organizers, advocates, or any people who fight for the well-being of those around them into the conversation around data and technology governance. 

Since writing the newsletter, lots of things have changed! The world has changed. What I know and feel about the world has changed. I’ve deepened my roots in Houston and grown in care for the people here in ways I didn’t know were possible (special thanks to my H-town readers, love you big).

I’ve also questioned myself a lot. Why am I so compelled by the ways that data can help us translate experiences and information, data movements, data flows? Why should we care that companies use extractive platforms to shape culture and society?

I hope to get into those questions through this newsletter but one thing is for sure: There is no longer any escape from the influence of tech platforms on our bodies, hearts, and minds. Tech companies that unilaterally govern these platforms do so in order to make it easier to extract everything that they need to build their future. 

They manufacture our consent for exploitation both of workers and of natural resources like conflict metals/minerals while designing and selling us more products, advancing rampant consumerism, and not to mention plotting the obsolescence of swaths of the workforce. 

Our data, and by extension our bodies, are the source of tech companies’ power to enact these harms. 

It’s more urgent than ever that together we develop an awareness and ability to act in response to tech companies shaping the future. That involves educating ourselves about how control over access to our personal and community data could help undermine their power and prevent further exploitation. 

{Personal data = Any information that relates to an identifiable individual. Different pieces of information, which collected together can lead to the identification of a particular person, also constitute personal data.} From the European Commission. 

{Community data = A new definition I’m proposing to capture data that pertains to neighborhood assets, infrastructure, behavioral patterns, movements, natural resources, lands, or places under the collective stewardship of a community.}

In order to build collective power to resist data extraction by systems of power of all kinds, communities can self-organize in similar ways to other political terrains to build political leverage. Just like labor unions can help people bargain for better working conditions, and just like land trusts can help neighborhood residents regain control over community land, neighborhood data boards or similar cooperative structures could represent a new form of collective bargaining for data at the local level. 

Community data is already a resource that is being collected, shared, analyzed, and stewarded by dozens of community organizations in every neighborhood. But without language or understanding of data rights, these organizations, which often already have strong ties and dialogue with local community members, might miss the opportunity to leverage control over data as an asset in relations with elite research institutions, local governments, or private companies that don’t act toward the community’s best interests. 

In 2022, I wrote a blog post and subsequent report for the Institute for the Cooperative Digital Economy about why local governments need to understand tools like data cooperatives as tools to fight tech companies’ extraction of public data for profit. During my research fellowship, I spoke with various state and local government workers and technology entrepreneurs to better understand what’s already being done to advance data cooperatives and other power-building frameworks to fight back against data extraction in communities. 

What I’ve learned, in addition to what I’ve written about in my research, is that without public demand for data rights, governments have few incentives to solve the problem themselves. 

Similarly, tech entrepreneurs won’t save us. Many data cooperative engineers I heard from were white men interested in decentralized governance for deeply esoteric or even personal financial reasons, working in isolated communities of practice with limited engagement even in academia, let alone in real world organizing settings.

We have to solve the problem of control over access to data in community. 

I’ve come to believe that public demand for data rights will be impossible to cultivate from any positionality other than one of solidarity and true participation in communities where data rights are being abused. 

For example, research conducted by Adrienne Williams at the Distributed AI Research Institute shows that companies like Amazon can use surveillance to enable wage theft of delivery workers; Delivery workers who in general are increasingly surveilled across companies by employers and even by doorbell cameras, many of which connected directly to police surveillance systems through backdoor agreements until Amazon updated its policies last January.  

But data rights are also abused in more commonplace and socially reproducible ways. Take this as an example: 

Many communities that were severely affected by COVID-19 may have received multiple requests to participate in surveys and studies estimating after-effects. But research has found that in the wake of disasters or crises, “Being overburdened with too many research requests and failing to see any subsequent changes following participation may cause individuals to experience research fatigue.” 

In other words, community members who are surveyed year after year about their lack of access to housing, food, transit, education, or other life-sustaining resources may continue to see the lack of access and become disillusioned with participating, i.e. refuse to share their data. This is a well-documented phenomenon with regards to Census nonresponse rates.

While most governments respond to this issue by trying to motivate higher response through more persistent surveying, it does beg the question: If the same questions are asked on a survey year after year and no change happens, why should community members fill out the survey again? 

Why should people continue to give up their personal information to governments that don’t listen and can't mobilize information into systemic change?

Or more aptly, can we choose who we trust with our personal information before being compelled to fill out forms and give our data to institutions?

Would building the muscle to refuse data collection help us begin to build leverage against the more overtly harmful companies and institutions that exist to abuse or punish low income people and people of color? 

Our right to control access to our data goes beyond clicking No on Terms and Conditions for apps or websites. We have to examine how our data is collected across areas of our life. 

We can stop cycles of harm not only by regulating tech companies’ third party data sales or creating consent or opt out mandates, we can also teach people to cut off this extraction at the source. After all, governments and especially law enforcement benefit from mass tech surveillance.

We may choose not to consent to being identified. We may choose not to consent to the exploitation of our data. 

Over the next few months, I will be writing on whatever schedule I can manage to provide updates about my explorations into local data rights education.

I’d like to explore what data rights means to people who aren’t “plugged in” to the latest tech industry news or developments in emerging technology.

And I’d like to start developing tools and resources for people who are organizing around anti-surveillance or data rights in their own communities to have more inclusive, accessible, and empowering conversations with people who are handling personal or community data on the ground. 

Thanks so much for reading this far. Just a few more words of closing…

This is exciting new ground for me and I look forward to diving deeper into what this work means in a groundswell moment where new ways of being feel closer on the horizon. I hope it feels that way for you too! 

Given that these are uncertain times with few ways or places for us to connect around these issues, I would sincerely invite anyone who feels moved by this work to write me directly or keep in touch at hello@katyaabaz.com. 

I am very proud to share that this research and action in community is being funded by the Voqal Fellowship, which has connected me to truly powerful guides in the practices of shaping and reshaping the world. I thank them all for their support!

Thanks for sticking with me. 💜

Somewhere in a major US city, a vendor is entering a bid to work with the local government at a public office building, wheeling a pallet stacked 3 feet high with reams and reams of paper. 

Many local governments in the US conduct their procurement programs, or their processes to purchase goods, on paper. Some are taking steps to digitize procurement to make spending more open and transparent, and others are re-evaluating whether their processes are even effective. 

Across the board, communities are often left out of local governments’ decisions about which products or services to buy. Sometimes this makes sense since local governments purchase everything from pens to playground sets, but other times it doesn’t. 

A significant part of governments’ operating budgets are spent through procurement. Globally, public procurement accounted for 12 percent of global GDP in 2018. That’s $11 trillion out of the global GDP of $90 trillion. US local governments could account for up to 16 percent of that, spending a total of $1.8 trillion in direct expenditures in 2019. 

What I’m saying is that local governments are spending life-changing amounts of money through processes that would be totally hidden from the public eye if it weren’t for the transparency gains won by advocates in the last 10 years. For reference, the City of New York spent over $30 billion on goods and services in FY 2021 alone. 

Even if local government spending is seen, it isn’t always felt. With this much money potentially flowing into communities in cities across the US, why don’t you feel the benefits? 

Well, there are reasons. 

The way public procurement is set up allows a handful of corporate players to dominate. 

Public procurement is really complicated. And when I say complicated, I mean cooooomplicaaated. 

Having such a high barrier to understanding how to even navigate the process means that local governments tend to work with the same types of companies. Companies that have infinite money and people to throw at the procurement problem. 

In a standard local government procurement process, these are the general steps you’d have to take if you just wanted to bid on a city contract: 

1. Register for an account on a city’s procurement portal. Already obscure.

2. Register for alerts about upcoming procurement opportunities that match what you can offer. If this is even an option. 

3. Once you find an opportunity, read the RFP and assemble your materials. Contingent on being able to read the RFP. 

4. Make sure you are insured. Not very affordable if you’re running a small business.

5. Figure out how to actually submit the bid. Yes, that’s right, where you read about the opportunity is not necessarily where you submit your bid for the opportunity. 

I don’t think I have to explain how this creates an exclusionary environment where the same handful of big, strong companies with a never-ending supply of people on the bench can bulldoze their way to get these bids submitted and win (looking at you, Deloitte). 

I’ve worked on projects with the Open Contracting Partnership and the Government Performance Lab to try to deconstruct some of these extremely hard-coded processes to allow a more diverse range of smaller, local vendors to benefit from procurement, but it’s really tough work that requires leaders to stick their necks out and invest in serious, big, sweeping reforms. 

The reality is that unless local governments take intentional steps to tailor procurement processes to people who are excluded, small businesses don’t stand a chance. That means fewer procurement dollars flowing into local communities and more flowing into the pockets of executives at companies that are more accustomed to logistical nightmares.  

But while we might not see our friends or neighbors doing work with local governments, we might still have to live with the consequences of local governments’ purchasing decisions. 

We actually do feel the impact of public procurement in our lives all the time… 

But it might not be a positive impact. 

Say your city found someone to build a playground through the procurement process, and then the monkey bars fell down after too many kids piled on at once. Now, you’re worried about the other playgrounds that the vendor has built around the city. What do you do? 

You could call the Department of Parks and Recreation, given that they’re the “buyer” on the city side, and let them know their construction firm didn’t do a good job. They might write your comment down on a complaint form and file it away in a cabinet. They might enter your comment into an Excel document. 

But then, when the Office of Youth Engagement wants to help build a playground nearby at a local school, they might choose to work with the same vendor. They don’t have access to Parks and Rec’s filing cabinet or their Excel sheet. 

You could try to contact the city’s procurement team, but they’re not used to hearing from the public, and they probably don’t have any kind of feedback form. They’d probably refer you back to Parks and Rec. 

But unless someone on the procurement team knows about the Bad Playground Builder, the Office of Youth Engagement is going to look for the biggest, lowest cost vendor for their next playground project. And, look at that, the procurement team says there’s one that has worked with the city before.

This is essentially what happens in government over and over and over again. 

If local governments don’t track outcomes from past procurement projects, they can end up contracting that same vendor to work with new departments despite their subpar performance. 

This has a serious effect on real people and their lives. On the ground level, this looks like kids at summer camp wondering why the city keeps working with food suppliers that deliver rotten meals; or people living in affordable housing wondering why the locks on the doors keep breaking.    

The effect of all this bureaucracy is that everyday people fall through the cracks. 

The good news is that there are lots of public servants and advocates who want these things to change. Many of them are working on building outcome-driven procurement, small business inclusion, channels for public participation, cooperative purchasing practices, and generally taking initiative to make necessary reforms.

But public servants aren’t always able to make procurement do good for the people because there are some people who like procurement the way it is. 

Inefficiency in procurement can open the door for corruption. 

Last September, Houston Mayor Sylvester Turner fired city housing director Tom McCasland after McCasland accused the mayor of funneling $15 million to a favored housing developer.

McCasland said the vendor was pre-selected to win the contract through the city’s procurement process. He said that the project, which would provide 88 affordable units, was selected over a project recommended by the housing department, which would have provided 362 units at the same cost.

The company attached to the project was called Harbor Venture Group, which is run by… drumroll please… the Mayor’s former law partner Barry Barnes who the Mayor worked with until his election in 2015. 

There’s not a clear way to prove that the procurement was illegal. There wasn’t an overt quid pro quo, which usually happens in the form of campaign donations. The city can just say that the criteria used to judge the contract, which some governments publish but Houston doesn’t, simply showed that the 88-unit project was the right one. 

Plus, at the end of the day, many local governments in the US are legally required to award contracts to the lowest bidder, so if the cost was even marginally lower, that’d be enough of a reason to go with the mayor’s preferred vendor. 

In general, there are a million behind-the-scenes “too complicated for you to understand” explanations local governments can use to justify their procurement decisions. That’s why transparency around procurement is such an important foundation of a functioning democracy. 

But transparency is only the beginning!

If these challenges in local government procurement got you riled up, then welcome to the party. There’s plenty more about how local governments buy things that need work. 

Since procurement is such a big topic, I’m taking suggestions on sub-topics to dive into in future issues! Drop a comment or email me your picks. 

Some ideas: 

• How are local governments opening doors for small or minority-owned businesses? 

• When can people show up to participate in procurement? 

• What can local governments do to get their procurement data in order? 

• How does procurement affect governments’ use of technology? 

Leave a comment

Linked Thinking

• Pay to play - Interactive feature by Zach Despart matching procurement data to campaign contributions and exposing corruption in Harris County

• How Illinois is building equity into procurement - Reporting by Beeck Center Storyteller-in-Residence Ashleigh Fryer on procurement reform in Illinois, supported by our Data Labs program 

• Can open contracting hold smart cities accountable - Panel discussion between me and some friends at the NYC Mayor’s Office of the CTO & Reboot

• What lies beneath - “Procurement challenges don’t end when a government agency selects a vendor to fulfill a contract.” A helpful blog post from Mark Headd on what comes after the contract for new gov tech

311 is a non-emergency phone line that allows residents to report issues like broken street lights, street cleaning, potholes, overgrown vacant lots, and other generally low-level nuisances. In any city that has a 311 line, the data catalogs these complaints. 

People like to work with 311 data because it can be automatically updated on a daily basis, and it’s connected to specific times, locations, and service codes, meaning it’s easy enough to analyze. 

In theory, it should be the perfect way for local governments to learn about what’s on residents’ minds and get a general indication of neighborhood well-being. The idea is that local governments could analyze 311 data to understand what their residents want and need. 

But residents don’t just use 311 to report potholes and vacant lots. 

People also use 311 to report houseless encampments, graffiti, noise, and loitering. As a result, 311 data is loaded with racial bias that often goes unexplored and overlooked when local governments use it to understand local needs.  

It’s important that 311 programs exist because they reduce the burden on 911 responders to respond to non-emergencies, and they provide residents with a way to seek help with everyday issues. 

But the data is too biased to shape how governments deliver services or design policies, and few local governments or experts are raising awareness enough to prevent this bias from reinforcing the inequities in how public services are designed and distributed. 

Too many local governments are using 311 data at face value. 

Local governments tally up the number of potholes to be fixed, look into areas with high counts of noise complaints, and try to clean the streets where people report trash. In a very tangible way, local governments use this data to prioritize where they provide services across their towns and cities in real time. 

Experts reinforce this practice by encouraging local governments to analyze 311 data to shape public services, including by using artificial intelligence to analyze the data and respond to resident requests, despite core issues with the data. 

But multiple studies have shown that 311 data is a more accurate predictor of geographic breakdowns of class, race, and neighborhood change than it is a predictor of a community’s actual needs. 

While, on the surface, it is a good thing that local governments use 311 to quickly respond to residents’ needs, research by Joscha Legewie and Merlin Schaeffer in 2016 showed that a higher density of 311 calls come from transitional zones where neighborhoods are in the process of gentrifying, and white residents come into contact with their new neighbors who are people of color. 

Research by Ariel White and Kris-Stella Trump in 2016 also showed that participation rates in 311 are negatively correlated with similar low-cost civic engagement activities like turning out to vote or returning the Census, and highly correlated with high-cost activities like contributing to political campaigns. 

Taken together, these studies paint a picture of who is using 311 the most to express their concerns and, by all appearances, it’s mostly rich White people. 

Since 311 intake doesn’t include collecting demographic data about the callers, there isn’t a way to prove exactly how many people of different racial and economic backgrounds are calling in. These trends could vary across cities. 

But we do know that across the country, Black communities, historically and presently, face organized abandonment and divestment from basic infrastructure needs like drainage and street cleaning. In fact, we have mountains of data other than 311 that prove that there are significant non-emergency needs in most low-income communities of color.

As I mentioned in my previous writing on dashboards, local governments often already have data that shows them existing lines of inequality and the resulting disparities. (See also: my favorite series from Houston’s One Breath Partnership on recurring geographic patterns of inequality like the Houston Arrow). Instead, leaning on 311 data to understand residents’ needs reinforces societal disparities in how public services are distributed. 

So why does 311 data continue to appear as a reliable indicator of community need?

Most local governments don’t have a way to vet data for bias. 

Organizers of a New York City Data Jam in 2017 posed a question that few governments ask to evaluate bias in 311 data: “Is this where the streets are in the worst condition, or is this where people are complaining about the streets the most?” (Thanks to the good people at BetaNYC, the materials are open, so you can explore the 311 data challenge yourself.)  

Tracking bias in local government data would first require better documentation, or metadata, on how data is collected, formatted, and analyzed. Then, data users could answer questions about the quality of the data for themselves. 

For example, a city planning survey might show that a majority of residents responded “Yes” to a question indicating a desire for the city to build more parking spaces. But after looking at the metadata, you might see that the language of the survey question was, “Would you like more parking spaces on your street to address the recent increase in parking violations in your neighborhood?”, which introduces bias toward a single proposed solution to a wider problem which respondents might be indicating an interest in resolving.  

Basically, your interpretation of data can always change based on the data’s context. All data carries some bias. So it’s important to understand that bias and document it for people who are going to use the data in the future. 

The CIVIC Data Library of Context is one project attempting to expose bias through metadata and encourage more ethical practices of data governance. In my team’s recent work for the Beeck Center’s Data Labs program, we brought experts from CIVIC in to speak with state government data officials about best practices for identifying and mitigating bias in their data. 

State and local governments should be investing time and resources to ensure that data is well documented, that steps have been taken to reduce bias, and that data is used in ethical and equitable ways. When it comes to addressing the bias in 311 data, we have to go a step further to understand the forces in real life that create bias in the data. 

Is 311 even working as a non-emergency helpline or has it been co-opted for neighbor-on-neighbor policing?  

311 is unequivocally a tool that matters for people to have access to their local government. But there’s plenty of unpacking to do around why one person would trust a 311 helpline, while another wouldn’t.  

People advocating for cleaner streets or better quality of life in communities of color might have lost trust in local governments’ responsiveness to their needs after decades of divestment. But people belonging to privileged groups like homeowners, campaign contributors, and gentrifiers might still use access to authority to wield power. 

Take Legewie and Schaeffer’s research on contested boundaries which shows how White residents use systems like 311 to get authorities to reinforce the social and structural boundaries between them and their non-white neighbors. 

Society’s tacit support for monitorial citizenship through platforms like Nextdoor despite issues with racist reporting has created an environment that is outright dangerous for people of color.

As scholar Apryl Williams noted in a discussion of her work last year, this culture has allowed White complainants to “perform racialized fear” and create potential life-or-death consequences for Black people and communities. As a result, cities like San Francisco have passed legislation classifying racist calls to 911 as a hate crime to deter monitorial behavior that could end in police violence.

In New York, a report by the city comptroller found that the city’s use of 311 to enforce social distancing led to a majority of summonses and arrests being issued in low-income communities of color. And while most 311 websites don’t direct people to report issues like houseless encampments, many people report them anyway. 

For example, in 2021, the City of Boston received 342 complaints about tent encampments that the data says they log as “information” rather than as a service request. This information could then be used by law enforcement to clear encampments in the future. 

But the same Boston 311 dataset from 2021 showed that while some “concerned citizens” were reporting their neighbors for parking their cars incorrectly or letting their dogs off leash, a majority of people were trying to report things like accessibility signage missing from public pathways or unsanitary living and working conditions. 

I can’t say for sure what a more democratic 311 system would look like, it could start with local governments stepping up to enforce rules against racist or discriminatory 311 reporting in the first place. It could start with making sure that 311 programs are actually helping people who need it the most.

Our communities are in need of a serious reckoning to shift the meaning of public safety away from surveillance and toward community care in general. By beginning to address the roots of the biases influencing 311, the data might, maybe, possibly, one day become all it’s cracked up to be. 

Have a relevant data project to share? Want to be connected with others working on this issue?

Leave a comment

LINKED THINKING

• When Race Leads to a Call to 311… - Coverage in the Atlantic on the demarcation of boundaries between racially homogenous neighborhoods emerging in 311 data. 

• White Surveillance and Black Digital Publics - Dr. Apryl A. Williams and Dr. Allissa V. Richardson on White vigilante-style surveillance of Black people in public spaces and the digital artifacts that contribute to collective action in response to this surveillance.

• Peoples Tech Assemblies: Democracy and Access - Fireside chat on engaging in local government, hosted by BetaNYC in collaboration with the NYC Office of the Public Advocate. 

• From Data Criminalization to Prison Abolition - “A dominant mode of our time, data analysis and prediction are part of a longstanding historical process of racial and national profiling, management and control in the US

I’d like to facilitate more conversations between folks who reach out through this newsletter! I’ve been inspired and energized by the lessons many of you have shared, and it feels such a waste to keep them all to myself. If you want to engage more or make Civic Source more engaging, please reach out.

Most of the time, innovation projects that aim to tackle houselessness make me go into skeptic mode. There are some spaces where data problems just aren’t the biggest problems; there is no amount of innovation that would end houselessness.

But why is it so hard for data to at least play a meaningful role?

For one, most communities already spend a lot of time and resources managing and maintaining Homelessness Management Information Systems (HMIS). HMIS is a federally mandated data system that every community receiving funds to tackle houselessness must build and manage.

As national housing nonprofit Community Solutions wrote in a 2018 impact report, “HMIS was imagined long before we truly understood what we would need technology to do for us on the ground.” 

HMIS is just one of many mandates from government funders and philanthropists that leverage data to prove that communities are effectively using funds.

As a result, most community service providers are stuck spending time and energy using data to track, monitor, and report on people without houses, instead of exploring how they might use data to get more people into houses.

HMIS systems centralize power and resources in a space that needs more decentralization, collaboration, and empowerment to solve the problem at hand. 

Functionally, HMIS exists to track the availability and uptake of social services that are available to people experiencing houselessness, and to report that data to the federal government. Alliances, coalitions, and large-scale nonprofits get big tranches of funds to administer HMIS systems according to specifications handed down from HUD. 

Centralized data is important. The biggest benefit of such a large-scale data system is that administering it requires convening providers so they can start working together. And without nationwide data systems, we would know less about the scope of large-scale issues like houselessness.

But HMIS systems don’t necessarily count every person experiencing houselessness, since they only register people who interact with services. That’s why HUD also runs the annual Point-in-Time Count, a survey conducted by block-walking volunteers, which is known to severely undercount the number of people who need services.  

And as you might imagine, the HMIS specs developed by HUD to serve every community in the country don’t always meet the needs of the local service providers. As a result, providers who have access to HMIS data don’t always find it useful for refining or improving their services, even though they spend significant time and energy maintaining it. 

So, most providers collect their own additional data, tailored to their needs to better understand who they’re serving.

Some organizations are better than others at building relationships or tracking who they encounter through services or person-to-person interaction. They can enter data-sharing agreements to share good data with other organizations in their community of care, outside of the HMIS. 

Data-sharing is a major key to unlocking better housing services in the U.S. Multiple different providers have to work together to move someone into the housing that’s right for them. They might also need help getting a job, applying for disability insurance, or treating substance abuse or mental health issues. But sharing data to coordinate a complex web of services is not always a funding priority.

We’ll struggle to use data to coordinate communities of care as long as we avoid the elephant in the room. 

Our modern system of care is built on a legacy of paternalism and disdain for poor people, Black people, immigrants, and other marginalized groups.

For decades (since the uptick in houselessness after budget cuts in the 1980s under Reagan), our system has treated people without houses like invalid members of society who live on the street because of their own moral failings.  

Our current systems of gathering and sharing data about houselessness still stem from that punitive legacy. 

Much of the data that is collected isn’t actually designed to end houselessness, it’s designed to perpetuate a system of monitoring funds. Governments and private philanthropies alike use data reporting to make sure that community service providers aren’t abusing funds.

By continuing to gather data about issues that frontline workers and people with lived experience already know about, funders accentuate the reality that they don’t trust the people they’re funding and serving.  

But there are bottom-up solutions out there. In 2015, Community Solutions launched a campaign called Built for Zero that encourages service providers to pare down their data efforts, and focus on creating a local “By-Name List”.

A By-Name List is a real-time register of every person experiencing houselessness in each community. In the Built for Zero framework, providers can stick to collecting the minimum amount of information that’s necessary to get someone housed, and must secure that’s person’s consent to be tracked.

It’s a promising effort to functionally end houselessness that continues to have new communities signing on. 

Efforts like these give us a helpful launchpad to have more pressing conversations about how data is used to address houselessness, including building more structure for people to exercise their data rights.

People without houses deserve to have control over access to their personal information. 

Anyone who works with houseless communities has seen that some of the most reluctant people to enter shelter or transitional housing are people who seriously don’t trust public systems. If we respected their wishes, we would give them an opportunity to opt-out of tracking by public systems, too. 

Some might argue that, given the choice, too many people would then opt out of the system, making it harder to deliver services. But maybe that’s for the better. We should be able to build systems that people actually trust. 

In many small and mid-sized communities, houselessness may affect a few dozen or a few hundred people. Building data infrastructure to surveil this group of people is arguably much more cost-intensive than hiring enough people to learn their names. 

In an ideal world, each community of care could establish governance principles that would let people who are receiving services know exactly how their information would be used. They could legally share their personal information with trusted providers who would then be able to share that information with each other. 

In that ideal world, providers could use data without the fear that they’re disclosing or sharing too much. They could share data with one another openly to the extent that it meets the needs or demands of the people receiving services. This is the potential power of giving people agency over their own information in their communities of care.  

Despite the liberatory potential, most data projects are so constrained by entrenched power dynamics that they don’t really stand a chance. 

There are few ways for communities of care to use data that don’t come into direct contact with funding power dynamics that dictate how data is used. Most of the good ones use data as secondary tool to support strong community relationships, with an emphasis on building trust, dignity, and informed consent.

But these solutions go against the status quo. In resource-limited environments, communities of care often don’t have enough support to experiment safely with new data projects. (The keyword here is safe experimentation, because in general we need to stop testing brand new ideas on vulnerable people. It’s gross.)

I want to learn more from people who are working on data in this space. A lot of my ideas about just uses of data in this space come from liberatory social workers who remind us how our systems are steeped in legacies of criminalization and punishment that cut us off from one another.

All I know for now is that until communities have the power to create more just systems of care that ensure dignity for the people they serve, technologies that attempt to solve the gaps in ending houselessness will always fall short.  

Have a data project to share? Want to be connected with others working on this issue?

Leave a comment

LINKED THINKING

• Abolitionist Social Work: Possibilities, Paradox, and Praxis - The Network to Advance Abolitionist Social Work (NAASW) and Haymarket Books host a panel about the possibilities of abolitionist social work.

• This is Our (Caring) Revolution - An episode of the On Being podcast with Ai-Jen Poo about the overlooked societal value of caregiving work.

• Research Shows “Housing First” Works in Denver - One of the many pieces of research confirming that the best way to get people out of houselessness is to give them houses.

• 4 Communities Have Ended Chronic & Veteran Houselessness - A map of the communities that have ended chronic and/or veteran houselessness. It’s possible!

As of the most recent issue, Civic Source hit 300 subscribers and had an average open rate of 56 percent per post!

When I started planning to write Civic Source, I knew I’d be inspired and encouraged by people around me who care about building collective models for data and technology reform inside and outside of governments. 

But I did not expect the astounding level of engagement and support I’ve felt from this community.

I want to take a moment to thank you for being an early reader of this newsletter. I hope you’ll continue to engage and challenge me to provoke dialogue around issues you care about.

After each newsletter issue, I hear from two or three folks doing brilliant related work in the field, who I get to learn from and chat with. (I hope folks will continue to reach out in the future!)

While I like to write about the gaps and nuances of what’s happening in local data and technology systems, these friends are sharing their own perspectives on the issues and offering solutions through their projects; projects others should know about. 

In a first attempt to foster connection between readers of this newsletter, I’m going to be hosting a fireside chat at the Shuttleworth Foundation’s Flash Forward Fest on February 22 at 3 PM CT. You can learn more about the event and register here. 

I’m exploring other ways to connect people who reach out through this newsletter! For now, I’m making sure I remember your stories and give them credit as they influence my future writing.

If you would like to be connected with others who are working on projects that I surface through this newsletter, please reach out by replying to this email or leaving a comment!

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Since this is a ~*special issue*~, I’m sharing a peek into my other passion… movies 🤓. When I was planning out this issue, I wanted to share something that shows why I’m personally invested in local government as a site for meaningful change and exploratory futurism. 

TV shows like Parks and Recreation that highlight the absurdism and high emotional stakes of local government have always really made me laugh and made me feel a little too seen.

I’ve always thought that the social dynamic they’re laughing at — what happens in local government — is what happens when people wield a precise and localized power while very few people are watching. 

I’ll admit that as a college student working to support local elections in Southern California, with very little stake in the game, I loved having a front seat to the theater. 

But there’s more to local government than paper-pushing melodrama. In my own work, I’ve met people who inspire me by fighting within their government organizations against decades of embedded practice to change things for the better.

It’s rare that I find content that captures the balance of absurdism with the dignity that comes from investing in public service in your community. 

Ikiru is a movie co-written and directed by Akira Kurosawa, renowned Japanese filmmaker and artist. With a distinctive style in black and white, Kurosawa’s movies take a painter’s eye to cinematography, using light and darkness to convey depth in ways that we might take for granted in today’s overblown cinematic landscape.

Stills from Ikiru via FilmGrab

In the movie, lifelong bureaucrat, Kanji Watanabe, who has never missed a day of work in his life, finds out he has a terminal illness. On his last day, a group of mothers come in to protest waste being dumped on land in their neighborhood, demanding the space be turned into a playground. 

Exasperated, Watanabe sends them to another department and leaves work for good, suddenly losing his desire to continue plugging away within the city’s bureaucracy.  

Watanabe tries to find various ways to cope with his last days of life. He spends time with his family, he explores nightlife with an intriguing novelist, and he meets a young coworker who is leaving the city govermnent herself to pursue a new passion. 

Without giving too much away, Watanabe tries and fails to escape the sadness of knowing life is ending, but eventually finds a way to leave a lasting legacy through his position at work.

He returns to the office for a last hurrah and his colleagues are left pondering the meaning of his triumphant return. He remembers he’s able to make a difference.

It’s a movie that tells the story of finding meaning in public work. The movie specifically points out the jurisdictional web that public servants can get trapped within, constantly referring people to other departments to get the help they need. 

In the end, the solution rests in Watanabe’s decision to do things the messy way, to make things happen without following an ordained procedure. He chooses to honor relationships with members of his community over the bureaucracy that ruled his life before.

Still from Ikiru via FilmGrab

Maybe it’s the masochist in me, but I love the idea of toiling in obscurity, or rather I love those who do it proudly. I think it’s those of us who work toward a collective vision in small and important ways who are truly essential in building a better future.

It’s sort of romantic. And that’s what this movie is saying. There’s a romantic life that can be found in serving your community.

Ikiru says there’s a legacy to be made in public service. Today, I think that legacy is the future we leave for others. In local government, we have a chance to take joy in caring for those in our community who will come after.

Hope you enjoyed this special issue. As always, feel free to reach out to schedule a chat or learn more about upcoming issues. Thanks for reading! 

If you’re someone who builds tech for governments or civic organizations, you’ve been asked to build a dashboard more times than you can count. Everyone wants a dashboard. But some have started to call the logic behind dashboards into question. 

In 2015, Shannon Mattern wrote a piece for Places Journal about the history of the urban dashboard, which also appears in her latest book A City is Not a Computer. She talks about how dashboards give decision-makers a sense of control when they can see their city through a white-washed panoramic view. 

Building on Mattern’s work, a recent paper by Jathan Sadowski blithely says: “Anyway, the dashboard is dead”. Sadowski explores the life cycles of two urban informatics dashboards built by a municipality in Australia to show that dashboards sometimes fail because of how harshly local leaders use them to try to succeed. 

So, you might ask, should we build dashboards or not? It seems that’s not the right question. 

Of course people need accessible ways to visualize and understand information so that they can act on it. But we need to ask how we use dashboards to ensure that they actually do inform and empower public servants and community members to change things for the better. 

Too often, local leaders use data dashboards to control a public narrative about how cities are doing while keeping an unforgiving hold on internal operations.

On the surface, local governments publish data dashboards to keep the public informed of their work or local issues. But deep down, they need dashboards to project the image that they know what’s going on and that they’re in control. 

Control is a necessary feature of governing, just as driving a car requires a wheel to steer. But when elected officials are the driving force behind designing public dashboards, the data might portray a controlled reality that is either convenient or beneficial for those in power. 

Historian Ruth Ben-Ghiat researches authoritarian leaders throughout history and wrote a book on strongmen, which provides the beginning of a framework for understanding how toxic masculinity and domination show up in our culture of governing.  

Strongmen value security and mask all forms of weakness. They value their own absolute knowledge, which dovetails with our current post-truth moment. As Ben-Ghiat says in an interview with TIME, “What they said was the law… You’re not supposed to have alternate interpretations of reality.” 

Sadowski writes, “Dashboards are meant to represent reality, but they can influence perception so much that they bend reality.” 

City councils, for example, commission citywide dashboards to construct their own top-down view of the city. Public-facing dashboards signify that work is being done through data points like Key Performance Indicators (KPIs). These KPIs are often determined by city council members, not by members of local communities. 

According to Mattern, “Dashboards are not intended to merely allow officials to monitor performance and ensure ‘accountability’, but also to make predictions and projections — and then to change the system in order to render the city more sustainable or secure or profitable or efficient.” 

Security, profitability, and efficiency are values that leaders might hold, but they might not represent the values of their communities, or even their employees. As Sadowski’s research showed, “People resented being browbeaten with KPIs. Thus, by extension, their ire was directed at the corporate dashboard; it became a despised tool of control.” 

When former Baltimore Mayor Martin O’Malley founded the nation’s first CitiStat program in 1999, the goal was to create a data-based process of performance measurement and internal accountability, which influenced other early data programs that used dashboards as control centers. But Baltimore’s program was based on NYPD’s CompStat program, through which Sadowski says “top brass” in the police department used ritualistic processes of humiliation on people who failed to meet performance standards and expectations. 

In other words, since the beginning, the message behind dashboards has been that city workers have to use them because the guys in charge want answers. 

Some public officials opposed these approaches because they sapped creativity. Using data as an accountability tool creates stressful environments where public servants are incentivized to manipulate numbers to reach higher-ups’ goals. 

Now that dashboard governance is a fixture of performance management across the country, dashboards give local officials a chance to play out their need for control instead of trying to use them to unearth public truths. 

People get frustrated when local governments try to use data to prove facts that are already deeply felt and widely known. 

Peoples’ trust in state and local leaders has hovered around 70 percent since at least 1997. But, probably because of the severe need and more focused attention on local governing in the last year, trust in local government reached historic lows in 2021.

Just like people’s trust in leaders can fade if they don’t see results, people’s trust in data can fade if they can’t find stories in the data that they can agree with or understand. 

As Michael Correll points out in his piece “The Spectacle of Dashboards”, too many dashboards “all proclaiming expertise but with differing data or implicit conclusions” will cause people to “either become increasingly skeptical of the value of expertise, or increasingly emboldened to come up with idiosyncratic explanations.” 

Building on this idea, Mattern says, “this disenfranchisement may be the goal in some cases.” Government leaders may benefit from convincing people that the data is too complex for them to understand, that the problems are too intractable, and that further data analysis or more advanced technology is the only way we’re going to find the answers.  

We see this over and over when governments try to map inequality through data. People call on local governments to close racial disparities and local governments respond by publishing more dashboards or funding more “data-driven” research on those disparities. 

But those of us who drive through the neighborhoods that suffer from divestment daily could draw their boundary lines like the back of our hands. 

In Houston, local housing advocate Zoe Middleton has spotlighted the “Houston Arrow,” a shape that shows up in every single map of socioeconomic data, covering the area where the wealthiest and whitest people in our city live. 

Patterns like the Houston Arrow show us that there’s knowledge of structural inequity that we’re unable to address through dashboarding or visualizations. For people in the community, it feels like local officials are seeing the same information appear on dashboards and in maps over and over again and aren’t doing a thing about it. 

But there’s a cottage industry around building dashboards and visualizations for local governments. There’s value in pretending that a new tool is all it would take for our local governments to finally get a handle on those big, intractable issues. 

As geographer and political scientist Brian Jefferson says, control centers offer an “innovative way of finding value in devalued urban populations and places.”  In this way, local governments exploit communities of color for insights every time they build a new dashboard. 

People start to get the impression that their local leaders are ignoring what they already know; that they’re constructing just-honest-enough political realities to be suitable for public consumption.  

But what if we could use information about our cities to bring a different future into being? 

Data dashboards might be useful if they gave more grounded, truthful answers in the present and asked more creative, speculative questions about the future. 

Mattern says of the modern dashboard, “The dashboard-as-talisman, when deployed in municipal buildings, on trading floors, and in operations centers around the world, is intended to aggregate data for the purposes of divining the future — and shaping policies and practices to bring a desired world into being.” 

In the current paradigm of data dashboarding, everyone suffers. Public servants feel the tension of being told they’re empowered by new data tools while checking over their shoulder to make sure their metrics still look good. The general public sees a sanitized version of what is actually going on, which reflects leaders’ priorities more than their own lived experiences.

If the current paradigm of dashboard governance relies on control, certainty, favorable visibility, and keeping public servants in line, the new paradigm has to feature creativity, uncertainty, and acceptance of the messy parts of governing, flaws and all.

Dashboards aren’t dead because they’re not useful tools, but because they’re a bandaid for lack of power and agency in local governing to solve big problems. They scratch at the surface of structural inequities repeatedly and without effect, like picking at a wound without ever allowing it to heal. 

We need a dose of the creativity that has been drained from public service in order to make data meaningful again. Making meaningful, accessible public dashboards and removing them from toxic cultures of performance management could make all the difference in how big problems get solved. 

Local governments tend to ask the same questions of their data over and over again. These questions are influenced largely by the motivations of the people in charge. 

• Where are the hotspots? 

• How much is it costing? 

• Are we meeting our leaders’ goals? 

• How have we performed over time? 

But when you speak to public servants themselves, there are questions they have that never make it into the public domain. 

• Why isn’t this program working?

• What are our constituents’ biggest barriers? 

• Where is our money actually going?

• Why doesn’t the community like what we’ve done? 

These are some of the meatier questions that would help cities get to the bottom of what is actually going on. And it would benefit people in the community to be able to see what their local leaders are truly interested in and working on, especially if they can lend a hand.

As Sadowski notes: “Stories of failure and frustration, of delays and dead ends, are extraordinarily typical features of the work done in government and technology. But these stories run counter to the narratives of innovation meant to sell smartness.”

Some cities are already showing that they have trust in their constituents’ abilities to help solve problems. The City of Cambridge publishes a Civic Innovation Challenge Inventory (updated January 29, 2022 ❤️) that invites people to use city open data to address challenges the city is facing, like “How are food trucks geographically distributed throughout the city?” or “How can Cambridge better support the performing arts?”

It’s not a dashboard, but it connects disparate sources of information in a tabular, visual way so that people can understand and act on the public information that’s available. 

Local governments should admit that sometimes they don’t have the answers; that the data they have is sometimes not sufficient to answer the questions people have about their communities. Decision-makers could use dashboards to allow people into that process of questioning, letting down the strongman facade that might is being right.  

When cities do need answers, they could let those answers come from the bottom up. As Mattern points out in her book, new models of visualizing urban information, like this work from Lydia Jessup, could prioritize themes of equity, environmental sustainability, and maintenance/care that center the built environment and lived experiences instead of more common smart city values. 

Dashboards are an abstracted concept, at this point encapsulating everything from an embedded map on a webpage to an augmented reality experience with data overlay. Getting caught up in the language is the wrong move, and putting all our faith in leaders to choose the right data is the wrong move, too. 

By flipping the script and allowing questions to become our new modes of public discourse, allowing people to provide answers about their own communities, maybe dashboards can live again. 

LINKED THINKING

• A City is Not a Computer - This newsletter is heavily influenced by Shannon Mattern’s work and I highly recommend checking out her book!

• Accessibility for Teams - Our friends at GSA and the Accessibility Guild have made a really great resource for designing accessible data visualizations. 

• Vaccine Questions and How Data Empowerment Happens - A piece by Michael Canares on how data empowerment requires an environment that allows questions to be asked and the capability of people to ask questions. 

• The Story of the Covid-19 in Favelas Unified Dashboard - An organizing effort led by favela-based collectives and allies in Rio to design and build a data dashboard to track the spread of Covid-19. 

• The Crime Machine Part I & Part II - Investigative podcast Reply All tells the story of the computer system that led to NYPD’s present-day misuse of performance data.

In part, that’s fair, because the climate crisis will not be over until we as a society renegotiate the structure of our economy and the dignity of life within it. But in the meantime, local governments can take meaningful steps toward climate resilience. 

Reducing car-centric infrastructure, creating green jobs, reducing waste, managing retreat from our shorelines, and improving public transit are all examples of local-level improvements that can help mitigate the climate crisis. And most local governments document these types of goals through climate action plans. 

Climate action plans should not be just another local policy that gets published and left to languish in PDFs on a city’s website. They can be tools for organizing communities around specific climate goals and ensuring that local governments stay open and honest about the actions they’re taking. 

Between 415 and 600 cities in the US have made climate action plans (and a few communities have made their own peoples’ plans), but data on their progress is hard to find.

Climate action plans are not written with data in mind, which sets us back in holding local governments to their word. 

Local governments often get away with publishing new policies without making plans to collect the data that would help them execute and evaluate them. 

Take this target from Houston’s Climate Action Plan: “Reduce barriers for using multi-modal transportation”. The plan mentions that the city will improve its bikeshare program to address this target, but makes no mention of any data about how the city will know if barriers have been reduced. The plan’s methodology shows that the city didn’t use any data about the current barriers to using multi-modal transportation to create the target. What are the barriers now? How will they improve? Where’s the data to prove it? 

Local governments can’t successfully execute climate action plans without data. But public servants often don’t have the right data on hand to evaluate or communicate their work. 

Let’s follow the example of reducing barriers to multi-modal transit. Evaluating this target would require setting a baseline for current barriers to using transit, for example: average distance from each household to a train or bike station, timeliness of public transit, cost barriers to affording transit, etc. 

These data points might come from transit authorities, housing agencies, economic development offices; multiple departments across the city that intersect with people’s use of public transit. But since most local governments have few full-time data experts on staff, compiling this data from across departments is difficult. 

There often isn’t enough person-power in-house to do the leg work that would make it easier for the government or the public to track progress on climate action plans. 

Leaders need to prioritize investments in data capacity to track climate issues.  

I evaluated 20 climate action plans from a randomly selected group of cities across geographies and population sizes to see how many include commitments to collect or improve available climate data. Not a single climate action plan had a significant plan to improve climate data so that progress on targets overall could be tracked. 

The City of Pittsburgh has the closest thing to a data commitment, stating: “Actions started as a result of this plan and the associated data to be collected will inform the next iteration of the Pittsburgh Climate Action Plan”. The City of Anchorage also does well by connecting specific targets to data improvements like “increasing data capacity” to measure progress or “improving inter-agency data sharing”. But at least seven cities of the ones I sampled don’t mention data in their plans at all.

When local governments publish 60-page climate plans in PDF format, people have to sift through mountains of text to understand what their governments’ targets are and how they plan to track them. 

But improving climate data at the local level has to mean more than just improving how data is used behind-the-scenes. Local governments have to publish data that people will understand so that they can participate in mitigating the climate crisis in their city. They can also support community efforts to track climate data to help reduce the burden on public servants to do it all themselves. 

Local governments need to work with communities to mitigate the climate crisis.

Recently, a new platform called Possible Place launched a pilot in New York City to help people better understand and influence their city’s plans and policies on climate change and sustainable infrastructure. People can use the platform to browse target-by-target through their city’s climate commitments. Community organizations can also use the platform to help compile the data from across public sources.

Local governments not only need to invest in building better climate data; they also need to ask people about the climate data they need and make that data public for communities to use. People are passionate about addressing the climate crisis. They would help do the legwork to help local governments stay accountable on climate if they had the right tools. 

In Houston, we’re lucky to have groups like West Street Recovery, which partner with researchers and community members to build a homegrown narrative about what’s happening in our neighborhoods throughout the climate crisis. Community organizations like West Street can help communities recover from climate disasters and prepare for the next ones. They help get community members engaged, and help the people hold the government accountable.

Every climate action plan should come with commitments to improve climate data at the local level. The climate emergency paralyzes people from seeking out possible ways to help, but cities can help create the on-ramps people need to get involved in their communities. 

While the responsibility for climate mitigation does not rest with individuals, there is power in the potential to collectively advocate on behalf of structural changes to our built environments that will reduce our impact on the earth.

LINKED THINKING

• Survivors as Experts - The latest research from West Street Recovery on the barriers to recovery from Hurricane Harvey from the perspective of residents living in low income Black and Brown neighborhoods in Northeast Houston. Donate to WSR here.

• Tallgrass - An essay by Robin Wall Kimmerer on the functionally extinct tallgrass, vanishing prairies, and the hope for a return through grassroots efforts. 

• Miki Kashtan on the Hurry Slowly podcast - “The question of our time is, how do I move towards community? How do I move towards creating shared risks with more and more people?” 

• Defining Environmental Justice Communities - Recap of a data user group meeting hosted by the Western Pennsylvania Regional Data Center on better open environmental data. 

• Open Climate Now - An invitation from the organizers of Open Climate Community calls to think about key values of the open movement (mutuality, autonomy, altruism, collaboration) supporting climate action.

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Survivors of domestic violence come to care providers and local governments for help in their hardest moments. But many survivors struggle to trust institutions with crucial information because of difficult perceptions or harmful experiences in the past. 

We can’t solve problems we can’t see. Organizations serving survivors have to work together, not only to help people in crisis, but also to understand and address the systemic prevalence of domestic violence. Survivors might encounter organizations that provide emergency housing, transitional shelter, or legal services. Some organizations work on prevention and education with specific groups like youth, monolingual populations, or certain religious communities, and some provide crisis counseling and case management in real-time. 

Trust dictates how much personal information survivors feel comfortable sharing when receiving services. Survivors need to know how providers are sharing their information, what organizations will do with it, and how they’re protected. They need agency to control the disclosure of their personal information, which could be weaponized in the hands of law enforcement or their abusers. 

The challenges around privacy, agency, and transparency facing survivors of domestic violence mirror the challenges facing society when personal data is exposed across personal and political contexts. While we in the data community might think big about what it means to trust someone with your personal information, or what it takes to build that trust, frontline workers who support survivors of domestic violence confront that question head-on every day. 

This week’s Civic Source is a transcript of a recorded interview between me and my friend, Elise Hansell, Policy & Grants Manager for the City and County of San Francisco’s Department on the Status of Women. 

Tell us a little bit about your work. 

I work for a city department in San Francisco that focuses on health, safety and economic empowerment for women and girls. My work involves local policy-making, legislative advocacy, research, and grant-making. I don’t provide direct services — our department is just six staff — but we fund a network of community-based service agencies that serve survivors of domestic violence, sexual assault, and human trafficking. Our department is a convener between direct service providers and local government agencies working on criminal justice, human services, public health, homelessness, transgender initiatives, the mayor’s office, kind of everyone. 

How do people enter the system across your network of partners? 

Everyone is doing intake. Survivors can come into contact with city agencies or nonprofits, sometimes because of domestic violence, other times because of related issues like housing or legal support. A lot of people think law enforcement is where survivors go, but really more than half of the people who experience domestic violence don’t report it. When they do, we see a higher number of people first reaching out to community-based organizations.  

And how well would you say those organizations are talking to each other? 

I work on the Family Violence Council which brings together city departments involved in child abuse, domestic violence, or elder abuse. We collect data from about two dozen agencies and publish a report about where people show up across systems to inform coordination. It’s good research, but it doesn’t cover the entire scope of coordination problems. For example, in our homelessness response system, there’s no way to coordinate intake for survivors of domestic violence. A lot of organizations have piecemeal referral systems that have been developed between their partners. The police department can partner with domestic violence agencies, and then those agencies have partnerships with homelessness response programs, and they can use federal resources dedicated to survivors to get out emergency housing vouchers. There’s a lot of cross-pollination, but there’s not really a clear understanding of intake across the board. I haven’t seen any evaluation of what information is collected about you if you file a police report versus entering through the homelessness response system or in a medical setting. 

How do you usually use data and information as part of your role? 

Our department administers a $9 million grant fund, so we collect data from our grantees. It’s hard to measure outcomes when people are doing crisis intervention, but we get baseline data for what services are provided and demographics of people who are reaching out for help. It’s helpful because domestic violence is so under-reported that there isn’t great local-level research. Data supports our advocacy to try to get more investment in prevention and intervention. And it helps address gaps in service provision or equity issues where people aren’t receiving support. 

Why do you think it matters for information about domestic violence to be public or accessible?

If you don’t understand the scope of a problem or where people are reaching out for support, then you won’t be able to reduce incidence or test out new intervention strategies to address the gaps. Domestic violence can become politicized so it’s very important for us to truly understand the issue instead of relying only on narratives or anecdotes. But those are important too! Looking past the quantitative data, there’s not a ton of qualitative data to tell us about survivors. It’s a double-edged sword because a lot of the work we’ve done trying to reduce the harm of law enforcement protocol has been built on anecdotes about groups that have been marginalized like immigrant survivors or trans survivors. For example, we’ve heard that monolingual survivors can be arrested when the abuser is the one who speaks English. Or in queer couples, it’s happened that both partners have been arrested when one is the reporting victim. 

Is it a police accountability issue for us to know more about those situations? 

Not just that. It also shows how important it is to have community spaces where advocates and policy-makers can come together and acknowledge that these things are happening. So even if the survivor isn’t going to submit a complaint to the department of police accountability, an advocate can come and share what they’ve seen to get us to reconsider a policy. 

So it’s important that quantitative information is used with qualitative information and that’s why it matters to have advocates in the room and able to respond to the data.

Yes, the data can get thrown around a lot, especially in political campaigns. Domestic violence got called out recently because some critics of the DA said he wasn’t doing enough to address it. They’re hard cases to prosecute so often when the police department or the DA’s office use the data, they’re throwing examples at each other. That’s where it matters if you have access to the local data but no understanding of how it fits together. It can create a lot of conflict. 

What are some risks survivors might face or think through when they share their information with institutions? 

This is something I think there’s not enough awareness around. A major consideration, whether or not it’s top of mind for survivors, is that when police respond to a call, details of your case or testimony will be made available to the defense counsel who represents the abuser. That could put victims at risk. When we share tools with law enforcement, we try to be really clear and let the survivor know that answers will be shared in court. Also, in California if you’re a survivor in an emergency room or medical setting and you’re disclosing domestic violence with serious injuries, the medical professional is a mandated reporter to law enforcement. It’s a blanket generalization, but most things that go through law enforcement are not survivor-centered. Places where people are seeking out shelter or services are more trusted. In organizations that receive funding through the Violence Against Women Act, there’s a confidentiality privilege between the institution and the survivor. But I think a gap is that a lot of information in the Homeless Management Information System, where people are tracked for housing services, might not have protections for survivors. I think there’s an effort to create another housing coordinated entry system that would have different protections for survivors. 

Just to paint a picture, what is front of mind for survivors when they’re sharing information with providers? 

I think for some survivors, there’s an awareness of how government agencies like Child Protective Services work and that there are government-mandated reporters. For example, there’s a big movement to end Failure to Protect laws. Because if police are responding to a home and there are children present, a survivor may or may not share all of the important information out of fear that something might happen to the custody of their kids. Undocumented survivors might also be really fearful of having their information in public systems. 

What are some of the safeguards that the providers in your network have in place to protect survivors’ privacy? 

You can have your name redacted from police incident reports involving domestic violence. There are also some time-limited informed consent rules where you can share sensitive information like case notes with a provider for a specific meeting with time-limited constraints. Oftentimes in family court, you need a police report to justify an emergency protective order, so requiring that type of proof means more systems are getting access to sensitive information. But another safeguard is that if people are trying to get accommodations at work related to their experience of domestic violence, you can have a service provider write a letter to your employer certifying the experience of abuse so you don’t have to reveal a police report to your supervisor. 

How can individuals speak up for themselves to protect their information? 

I honestly don’t really have a good answer. We have the redaction from police reports and survivors can take some steps to seal records. For specific projects like a task force, we might have seats for survivor voices so we’re able to engage people who have been directly impacted. But I don’t think there are that many mechanisms. 

What do you think the city or your partners could do to protect the survivors’ privacy? 

There are different layers. We have to understand the data infrastructure and the different entry points for survivors. It’d be really helpful to have someone at the city who has a clear understanding of how these systems interact, what can and can’t be shared — like a process map. I work with a lot of agencies but I don’t think anyone has a bird’s eye view. On the individual level, there’s variance in the training we have for frontline workers who are doing intake. We ask how frontline workers are working in trauma-informed ways but we could also ask how they’re abiding by the laws around confidentiality. There’s a lot of room for human error. 

Right. This is a really complex space and trust is a fundamental factor in whether we have the information to solve the actual problem of domestic violence. It’s also an area where the data can be so politicized that when we in the general public hear about it, it’s being used by lawmakers or policy-makers to put a stake in an agenda. But then there’s not enough investment in how the trust actually gets built and how the data gets better. 

Yeah, often it’s used and it’s not really invested in. Since trust is so fundamental and we don’t have a big picture understanding of these data or privacy laws, a lot of advocates err on the side of being overly cautious. And maybe they’re not being overly cautious, maybe they’re just being wise, because there isn’t a structure. The reason people go to community providers is that they know their information can be more protected there. Although I think it is really important for advocates to be highly protective of that, we are also going to need to work together. 

Have thoughts about this topic? Know of someone whose work should be in the spotlight? I want to hear about it.

Leave a comment

LINKED THINKING

Mapping referral pathways for survivors - Hera Hussein and her team at Chayn are researching shared data infrastructure to provide a single source of truth for survivors. 

#DefundPrisonsDefendSurvivors campaign - Survived and Punished, an organization co-founded by abolitionist thinker and writer Mariame Kaba, fights for the 90 percent of incarcerated people in women’s prisons who are survivors of domestic or sexual violence.

We are imagining a world we have never seen before - adrienne maree brown’s keynote for the St. Louis Racial Equity Summit in 2021. 

“Handle with Care: Domestic Violence Safety Planning in the Age of Data Privacy Laws” - A paper by Jenny Wu on the landscape of consumer data privacy laws and the gaps around tech-enabled intimate partner abuse. 

In 2019, I spent a few weeks working with a team to interview people who buy food through the City of Philadelphia's purchasing program. I didn’t ever expect that in trying to improve local data, I’d be in kitchens, food warehouses, and shelters learning about how food moves around a city.

The project was a collaboration between the Open Contracting Partnership and the Sunlight Foundation. We wanted to help the City learn how publishing open data about purchasing could lead to providing better food to residents. 

When we set out to talk to residents, we had a list of questions: who was being contracted by the City to provide food, who was serving the food the City was buying, and who could speak to its quality? And importantly, what did community institutions working on food already feel about the issues? 

We talked to local food cooperatives and people in the food industry to understand whether or how they view city purchasing (also called public procurement). We interviewed people serving food at summer school programs, temporary housing shelters, and juvenile detention centers. If our project had been scoped at a county level — which it wasn’t — we would also have interviewed people serving food at prisons, jails, and schools. 

What we learned was that the City could take steps toward serving healthier meals by: a) overhauling data systems so they could start publishing the right data about what they’re buying, and b) making the bidding process for city contracts more accessible so that high-quality local food providers could start working with the City. 

Across our interviews, people were interested in seeing the City lead a shift toward healthier food supply chains in Philadelphia. But old school processes for purchasing were making it hard for leaders to execute on that vision.

If people think about public procurement at all, they likely don’t think about food. In the Twittersphere and among my colleagues, there’s a growing focus on reforming how governments purchase technology, which matters because governments increasingly deliver services digitally. But food, which brings people together and sets the foundation for community well-being, is just as essential as anything else cities buy. 

By looking at how governments buy food, we can start to understand how systems like procurement can affect public health outcomes or inequalities. 

According to research by Feeding America, food insecurity affected one in five children in the US during the pandemic and one in seven adults overall. The disaggregated data shows that Black, Latinx, and Native American families face food insecurity at least at twice the rate as White families. 

While food that’s purchased by local governments is just one link in the chain of food insecurity, city food programs serve people at some of their most vulnerable moments. 

Local governments, by necessity, have to fill the gap created by food apartheid. Government institutions feed children who qualify for free or reduced lunch, they feed people or children who are incarcerated, and they feed people who are without stable housing.

At a community summer meals program where kitchens primarily serve kids who get free and reduced lunch during the school year, staff said that their food vendor’s performance was unpredictable. Sometimes, a number of pre-packaged meals for their kids would show up damaged, meaning they were unsafe to eat. Other times, food would arrive spoiled or they’d receive the wrong order. 

Everyone understood that mistakes happen, but the problem was that there were few ways to let the City’s procurement department know about vendors’ recurring issues. The unpredictability would cause some people working at youth programs to avoid city procurement altogether and buy food for their kids out-of-pocket. 

This isn’t just a problem in Philadelphia. The vendors who were supplying food to the City during our project were a few of the same ones who supply a majority of local governments and large-scale food providers nationwide. 

Companies like Aramark, Sodexo, and US Foods regularly contract with cities like Houston, Chicago, and Los Angeles (which you can find by searching their contract databases of varying quality). These companies don't prioritize selling good food, but instead prioritize delivering quantities of food, trucked across the country on time, at a low price. 

Most local governments are legally obligated to buy products from the lowest bidder, regardless of quality, across all types of procurement. That means most cities are hardwired to prioritize low cost over actual value.

When local governments overlook value in their purchasing processes, they miss crucial information about communities’ needs. Giving food contracts to the lowest bidder means prioritizing the needs of taxpayers who want to save a buck instead of focusing on the needs of those who are eating the food the government is buying.

Understanding the value of what local governments are buying means having multiple and varied types of information captured in procurement data systems.

Philadelphians voted to amend their city charter to allow “best value” procurement in 2017. But transitioning to “best value” would mean overhauling tech platforms that are primarily designed to track data points like cost and timeliness, not necessarily vendor quality or feedback. After our research together, Philadelphia decided to start using the new “best value” rules to buy food and improve the quality of contract data published on their data portal. 

Molly Riordan is a Good Food Purchasing Coordinator at the City of Philadelphia. Philadelphia is one of just four city governments in the country participating in the Good Food Purchasing Program, which “transforms the way public institutions purchase food by creating a transparent and equitable food system.” 

Like any champion for innovation in cities, Riordan was one person doing the work of many to compile insights about food procurement, start hiring better vendors, and improve processes for getting food to people who need it. She has continued working with the city’s public health and contracting departments to update Philadelphia’s nutrition standards and other departments have responded with initiatives like smart city challenges to meet food needs during the pandemic.

In too many cities, sweeping reforms rely on the legwork of individuals without sufficient investment in overhauling the systems they’re working within. 

Better data systems could save individuals from doing ad hoc work to understand how processes like food purchasing are working. 

Local governments have to digitize their procurement processes so that information is standardized across departments and easier to publish. And it’s essential for local governments to publish procurement data online so that their communities have an opportunity to give input on what they’re buying.

But here’s the real situation: Most local governments are not able to answer basic questions about how vendors perform over time using their current contracting data systems. These are just a few questions that data systems should be able to answer about contracts, vendors, or products to buy better food: 

• Which vendors have consistently delivered damaged, expired, or spoiled products?

• How many times has a contract with a vendor been renewed, and why? 

• How many different departments are buying from the same vendor?  

• What are the public health outcomes resulting from the city’s purchasing choices?

The more public data systems can be improved to answer these questions, the better the data on cities’ open data portals will be.

More transparent data about food purchasing won’t make a difference in food injustice until leaders take real action to get better food into marginalized communities. And we have to hold them accountable. But having the right public information to track bad vendors and pressure cities to shift to healthier local alternatives could mean a person who’s struggling gets a better meal in their hardest moment. 

Making that shift toward tracking and publishing better procurement data has to come from a place of understanding that people deserve to know not only how their money is being spent but how people in the government’s care are being treated. The most important part of reforming data in local government is knowing that in the end, the data has to serve something greater. 

Before you go further, I want to hear from you: Is there a topic you want to see in a future CS post? Is there a story you think should be heard? Send me your thoughts! 

Leave a comment

LINKED THINKING

It’s Not a Food Desert, It’s Food Apartheid - An interview with food justice activist Karen Washington on community organizing, urban gardening, and the food justice movement.

Value Over Cost: How Philadelphia’s Committing to Better Practices - Former Sunlighter Becca Warner on our work in Philadelphia and main recommendations. 

Piloting Open Contracting Reforms in LA and Philly - Short public summary of our report written for the cities of Los Angeles and Philadelphia about proposed open contracting reforms. 

18F’s De-Risking Guide - Geared toward large-scale government software projects, these guides from 18F have a lot of gems of advice that end up being about procurement. 

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Thanks to a widespread culture of secrecy-by-default in public service, departments in local governments struggle to work with one another. When intersectional issues come up in peoples’ lives, governments force people to categorize their problems by jurisdiction. Anyone who works with governments is familiar with government departments and their “silos.” Silos are social and cultural barriers that keep city departments from working together. They’re the issue I hear the most about when talking to government employees about the reasons they don’t collaborate.  

The tough truth is that most cities don’t tackle big, cross-cutting policy issues because working across departments or agencies just seems too hard. 

The culture of ownership and exclusivity in local governing has its roots in the early 20th century, when governing was a men’s game that happened within the confines of a “smoke-filled room.” But people running local governments today have to respond quickly to intersectional social and economic issues. They have to step outside of their comfort zone by bridging solutions across departments to effectively respond to peoples’ needs. It’s virtually guaranteed that every department in every city across the country could do better at collaborating with others. 

When they do step out out of their comfort zones and ask for feedback from communities, policy-makers want to hear feasible solutions. For example, many local government leaders have criticized communities’ demands to divest from policing and reinvest in communities because there aren’t clear pathways to meeting those demands. Meeting that demand would require police departments, health departments, housing departments, community development departments, regulatory departments, county and local traffic enforcement, and other agencies to work together to build a new system of care. It’s understandably a tall order. But local governments will say that silos are holding them back.

When cross-cutting policy initiatives fail, local governments can blame individual departments or agencies for blocking progress. “So-and-so doesn’t want to share.” Then, they try to approach the problem backwards. They think that if people had automated ways to share data, they’d start working together to solve some of communities’ bigger, stickier challenges. 

When governments buy technologies that advertise a one-shot solution to collaboration, they paper over cultural barriers that still lie underneath.

Government technologists who have tinkered with this approach can attest that tech-first solutions don’t work. Governments that attempt to share data across departments without building solid relationships for collaboration and transparency up front will hit roadblock after roadblock and wonder why their projects struggle. Governments have to build tech and data solutions by building rules and relationships that make sure data is used responsibly first. Good governance and person-to-person collaboration can improve how effectively data-sharing and data integration improve public programs and services. 

In the future, filling out one form for benefits through a government department might mean you’re automatically alerted about other benefit programs you’re eligible for; Good governance would ensure that your personal data is being protected  throughout that process. But if they’re not governed well, integrated data projects can expose people’s personal data to risk. Personal data that is linked across departments carries a fuller picture of a person’s life. Data can be leaked through weaknesses in cybersecurity, or public servants could use it in ways that individuals haven’t explicitly consented to. That way, we can ensure that governments don’t re-create the shameful state of data privacy that exists outside of government. 

So, how could it work? Getting people to collaborate and take the time to do it right often takes a directive from an executive like the mayor or a chief innovation officer. For example, the What Works Cities program requires a mayoral letter of support for cities to be admitted, and the State CDO Network’s Data Labs program requires a governor’s letter of support. Local governments are very hierarchical so leadership transitions and systems of centralized power can be enablers or blockers to the type of collaboration that would free up space for new ideas and innovation. 

But change can also happen from the bottom up. Public servants who work directly with communities, who serve people every day, can make the case for more inclusive practices. Public servants have the power to make sure that local governments involve people early and often in the design of new systems so that they have better rules and relationships to govern data in the future. 

I’ve seen that better collaboration inside local governments and stronger transparency initiatives go hand-in-hand. Good data governance and open data are two sides of the same coin. European cities like Amsterdam or Barcelona show us how cities can take civic participation seriously and build it into the foundations of their tech and data reforms. 

We should be able to demand intersectional solutions from our local governments that address the complex inequities in our lives. 

We can ask that those solutions start with openness and better, more responsible data-sharing. We can ask for departments to work better together, and after they’ve committed to transparency and good governance, we can design tech and data solutions together that can make it happen. We can look across cities, towns, and rural communities to find new models for collaboration and collective ownership in local government. And we can hope that our local public servants continue to ask hard questions and improve how they work.

Mariame Kaba opens her book, We Do This ‘Til We Free Us, with a dedication to her father, Moussa Kaba, who taught her that “failures are always lessons and that everything worthwhile is done with others.” It’s similar to advice I hear a lot around local government folks, a proverb that challenges Western individualism, “if you want to go fast, go alone; if you want to go far, go together.”  

LINKED THINKING

If It’s Not Public, Does It Even Matter? - Great blog post by Sean Boots on the culture of information hoarding in public service through the lens of an insider. 

Who Owns the World? - A brief explainer by Stacco Troncoso of DisCOcoop on cooperative governance and how the status quo in tech affects our ability to conceive of non-hierarchical, collective technology reforms. 

“Now’s the Time to Include Cooperative Language in Your Bids and Contracts” - Mariel Reed of CoProcure on the opportunity to coordinate purchasing across governments emerging through the pandemic. 

Collaboration in Crisis: Responses to the Pandemic - A report-out from the Open Referral network by Greg Bloom on how local agencies and community providers are coordinating to deliver better human services during the pandemic. 

The Filing Cabinet - When you think about how local government data originated with the filing cabinet, it’s less surprising that people aren’t used to sharing. 

Now You Know: Where Was the Original Smoke-Filled Room? - A good old historical fact-check, probably also the first cited use of, “I guess they let anyone in here.” 

Special thanks to Tyler McBrien for editing this newsletter.

Hopefully if you’re reading this, you signed up for Civic Source to hear more about how local governments use data and technology to shape communities. From building bike lanes to being the front line for climate crisis response, local governments have an outsized influence on peoples’ experiences of day-to-day life. And increasingly, the way they wield that influence is through the use of data and technology. 

But for most people, local government influence is so broad and deep-reaching that it escapes our understanding. Without understanding how local governments use data and technology, it’s harder for residents of cities, towns, and rural communities to have an active say in how technologies are used over time, and in turn, how their communities change. 

Public technology and data are political. But in order to shape their politics, we have to build a stronger collective understanding of what’s not working. 

I’ve worked with local governments since I was an undergraduate researcher at the Rose Institute of State and Local Government in college. I’ve had a front row seat to how the sausage is made, and while the details can be frustrating, awe-inspiring, and sometimes gruesome, I want to share what I’ve learned. In this newsletter, I’ll cover some of the common questions that come up in my work.

Some questions I hear often:

• Why don’t city departments talk to one another? 

• How can people influence city spending? 

• Why are there so many performance dashboards?

• What protections for personal data do cities actually have? 

• (Email me or leave a comment with other q’s you want to see covered) 

I’ll also share stories about how local governments are using data and technology to affect issues like flooding resilience, housing development, delivery of human services, or food distribution. But before I get to the core of those questions, I want to start at the very beginning. I want to talk about why it matters that we create better ways of knowing what local governments are doing in the first place.

The first time I walked into a city hall, I was probably 12 years old, visiting my aunt who worked for the Department of Parks and Recreation at the City of Houston throughout my childhood. It’s the place she has worked the longest since coming to the US from Colombia with my mom. When I went to visit her, she said, “We can just walk through here,” and skipped the sign-in sheet at the front desk as she flashed her badge. I remember being surprised about how casual security was, wondering if it was always possible for people to just walk in (I’ve since learned, for the most part, it is). 

There’s a certain open quality about city halls that differs from the stuffy guardedness of federal buildings. Over time I’ve come to appreciate that vibe, proliferating in city halls across the country. It feels right that city halls should feel like open spaces; that they should feel like they’re ours. 

It is possible that people aren’t banging down the door to get into local governments. Local governments have been in our public blindspot for a few decades. As of 2014, participation in local governing was abysmally low. But COVID-19 and the George Floyd uprisings have brought a renewed public focus to what local governments are spending on and doing. 

Truth be told, I’m riding the coattails of that trend, trying to get information into the hands of organizers and community workers who can change public technology and data systems for the better. But even as interest in local government has improved, the channels for people to participate in local governing haven’t. 

As of a few months ago in Houston, where I live and work, people had to leave a voicemail on a City answering machine with their name and phone number with no form of confirmation in order to give a public comment during virtual budget hearings. The City has struggled to maintain an open data program, so the County’s recent COVID-19 dashboards have been the strongest transparency efforts the region has seen in a few years. This matters because these public platforms have increasingly become the foundations of how we make decisions about our health and safety. They’ve become the main lifeline for people relying on rental assistance and eviction protections to get through the year. And they’ve become primary sources of information for grassroots organizers as they make the case to governments that we can build better futures free of policing and incarceration.

Data and technology systems have become the raw material that make up the relations between the governing and the governed. 

Governments have begun to acknowledge the importance of investing in those relations. For example, hiring tech companies that can conduct thorough user-centered design research and build accessible platforms for assisting with the delivery of housing assistance or social benefits. These platforms require maintenance by firms with strong civic values, at the very least. At best, they have to be built with empathy, with a commitment to public benefit, and an open understanding of outcomes, so that governing institutions and residents can have consistent and trustworthy two-way relations.

When local governments don’t update their public-facing technologies to enable stronger on-going participation, they have to rely on soliciting public feedback which can be a grab bag of sometimes useful, sometimes cherry-picked inputs from members of local communities. The foundations of participatory and open government don’t fit within the space of a resident feedback survey or a 30-minute interview. 

When local governments seek feedback instead of participation, they tend not to get the answers they want from communities about data and tech projects. What they get is, at worst, confusion or frustration with the systems as they are, and at best, curiosity about what could be done for the future. But peoples’ curiosity about public data and technology tends to go undernourished.

Through my work, I’ve spent probably hundreds of hours conducting or reviewing interviews with community members and representatives about the effect of public data and technology projects on their lives. I’ve spoken to first responders in Norfolk, Virginia about the quality of city data on flooding and weather forecasting, and to Native-owned affordable housing developers in Glendale, Arizona about the availability of permitting data that allows them to plan and build housing for local tribal communities. 

What I’ve learned is that people do want to know how data is going to be used in ways that affect them. They do want data that will help them do their jobs better, they do want to know whether their personal data is safe, and they want to understand whether local governments are using technologies that will make their lives better. 

While many local governments have taken huge steps toward better legislative and financial transparency, they still struggle to provide operational transparency. In other words, people can see what local governments regulate or spend on, but they still can’t see what local governments are actually doing. Involving residents in the everyday work of doing, of governing, is the first step toward enabling a culture of openness in local government. It’s the first step toward building what I end up calling “connective tissue.”

The connective tissue that improves trust between residents and the institutions that represent them is like the soil that allows new, collective ideas to grow. 

People don't know enough about the multitudes of data and tech systems that keep the lights on in cities. As a result, there's a gap in public engagement around the design and governance of those systems. We need more participation, more theories about what kind of innovation should happen in our communities. I’m inspired by a quote from Donna Haraway’s Staying with the Trouble: “We need stories (and theories) that are just big enough to gather up the complexities and keep the edges open and greedy for surprising new and old connections.” 

I hope readers of this newsletter will contribute their own theories and stories to build out new bodies of shared knowledge. I’m a long-time lover of cities, and my work on data and technology in cities has shown me that we can move through these systems together to imagine better, more just futures in our communities. I hope that the information I’m able to share here will help you plant a seed in the soil, a kernel for new ideas to grow. 

Thanks for reading, see you next time 💖

LINKED THINKING  

How to Map Nothing: Geographies of Suspension - Lecture by Shannon Mattern on the ways in which data captures or fails to capture situated knowledge about places and people.

Inhabiting the Negative Space - Commencement speech by Jenny Odell for the Harvard Graduate School of Design on the personal practice that informs modes of thinking that take communities and their realities into account.

“If we want to create humane, pluralistic futures, we need to nurture a different kind of curiosity.” - An interview with friend and brilliant change designer/teacher/practitioner Nicole Anand on the political potential of participatory design.

Beyond Open Data Policy - Some of my earlier thinking on moving toward more participatory open government from when I started leading the Sunlight Foundation’s Open Cities team. 

The City Halls That Defy Stereotypes about Mundane Local Government - Fun exploration of a new book, City Hall, which investigates the architecture of 15 unique city halls, covered by Linda Poon at CityLab.

Special thanks to editors extraordinaire Annie Yi and Tyler McBrien for helping launch this newsletter.

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