Academic Workshop

Trust and Doubt in Public-Sector Data Infrastructures

Call for Applications

Thursday, March 25, 2021
The application portal is now closed.

On Thursday, March 25, 2021, Data & Society Founder and President danah boyd and Executive Director Janet Haven will host an online academic workshop on trust and doubt in public-sector data infrastructures. Our goal is to bring together researchers who are thinking about government data from multiple nations/states, the infrastructures surrounding that data, and the epistemic battle over that data’s legitimacy.

Data & Society workshops enable a broad community of interdisciplinary researchers to dive deeply into topics at the core of Data & Society’s concerns. They’re designed to maximize scholarly thinking about the evolving and socially important issues surrounding data-driven technologies.

We imagine that this workshop will appeal especially to scholars in the fields of STS, organizational sociology, critical economics, and public administration. We would also like to encourage those working in fields focusing on specific populations, racial justice, and intersectional analyses to participate, including but not limited to African American studies, Latinx/Chicano studies, Native American studies, and gender/sexuality studies. We would be especially interested in hosting those doing empirical work, drawing from qualitative, ethnographic, or archival methods. Researchers engaged in critical data studies and policy analysis are also welcome.

Participation in this event is limited; submit your application by Friday, January 8th, 2021 (11:59 p.m. ET).

About the Workshop

Votes from the 2020 US Presidential election are in, and many are turning to what comes next for the incoming administration. A core concern across political spectrums is the need to (re)build trust in democratic institutions and government functions. One of the key sources of trust in democratic institutions—not only in the United States, but around the world—is public sector data, which acts as a bulwark against populism by undergirding evidence-based policy-making. And yet, both in the United States and internationally, we’ve seen public-sector data infrastructures come under attack through means that are both political and technical. Political interference has surrounded the methods and messaging of collecting and using census data, electoral data, climate modeling data, environmental protection data, public health data related to the COVID-19 pandemic, and more. This has meant that in each of these areas, there is rising distrust in both the data itself and in the institutions charged with collecting and protecting it. And at the same time, these institutions’ ability to act in a complex world, and to drive evidence-based policy, is reduced. Yet, trust—in both methods and integrity of the data itself — is essential for evidence-based policymaking to matter. Scholars of agnotology (the study of ignorance), especially those who have tracked battles over climate change, recognize this script. And as with media misinformation and disinformation, the United States has much to learn from other countries and regions that have long acknowledged and grappled with issues of trust and distrust in government data infrastructures.

For centuries, governments have built significant data infrastructures, upon which democracies and economies have been structured. Data has long been a source of power and state legitimacy, as well as a tool to argue for specific policies and defend core values. Yet, the history of public-sector data infrastructures is fraught, in no small part because state data has long been used to oppress, colonize, and control. Numbers have politics and politics has numbers. Meanwhile, public-sector data infrastructures have also grounded equity-oriented policies and advances in knowledge. Environmental justice movements benefit from climate data, voting rights efforts benefit from census data, and community health efforts from public health data. In the U.S., enhancing state data infrastructure has been viewed as a progressive agenda.

The purpose of this workshop is to bring together scholars who wish to grapple with the state of public-sector data infrastructures, with the longer term goal of establishing methods of protection, repair, and trust-building at a societal level. While governments collect data for innumerable purposes, we are particularly interested in the data infrastructure underpinning four epistemic efforts we see as operating at the crossroads of societal urgency and long-term democratic resilience: 1) Climate science; 2) Public health (e.g., pandemics, vaccines); 3) Democratic purposes (e.g., voting, census); 4) Economic modeling (e.g. labor statistics, employment data). In an American context, we are thinking about the data infrastructures underpinning federal agencies like the CDC, EPA, NOAA, BLS, HHS, Census, Department of Energy, etc. as well as the various state, tribal, and local data sources that operate within non-federal contexts.

Anti-colonial and anti-racist movements have long challenged what data the state collects, about whom, and for what purposes. Decades of public policy debates about privacy and power have shaped public-sector data infrastructures. Amidst these efforts to ensure that data is used to ensure equity—and not abuse—there have been a range of adversarial forces who have invested in polluting data for political, financial, or ideological purposes. Some of the work in this area stems from studies of agnotology, the study of ignorance, including ignorance that is purposefully manufactured. Others come from studies of conspiracy theories, as perceptions are twisted to fear data that might drive decisions for vaccination or climate policy. There is also, always, the threat that data might be directly manipulated or that certain metrics might be designed to ensure outcomes to benefit few. Economic measures require externalities, but what is center and what is an externality is a matter of values.

The legitimacy of public-sector data infrastructures is socially constructed. It is not driven by either the quality or quantity of data, but how the data—and the institution that uses its credibility to guarantee the data—is perceived. When data are manipulated or political interests contort the appearance of data, data infrastructures are at risk. As with any type of infrastructure, data infrastructures must be maintained, both technically and—crucially—socially. Data infrastructures are rendered visible when they break, but the cracks in the system must be negotiated long before the system has collapsed.

We are a U.S.-based research institute and we acknowledge that our framing is biased from our U.S.-centric perspective. That said, we are extremely interested in applications from researchers taking an international perspective or focusing on non-U.S. case studies. And we welcome papers that engage the ideas outlined above, but may take our thinking in a direction that we did not consider.

To provide you with a flavor of the type of papers that we imagine being appropriate, here are some questions that scholars might be investigating. This is by no means exhaustive.

  • How does the technical and bureaucratic design of government data infrastructures shape what states know? (e.g., StatsCanada as a national data source vs. the distributed approach in the U.S.; restrictions on U.S. federal access to data from state-managed programs like SNAP, etc.)
  • What are the (intended as well as unintended) consequences of using government data infrastructures as resources to enact policy? And how does this, in turn, shape government data infrastructures?
  • What data is purposefully not collected by the state in order to render certain types of knowledge invisible? (e.g., France’s approach to race data, Lebanon’s approach to refugees, U.S.’s approach to certain COVID-19 and climate data, etc.)
  • What are the ramifications of maintaining data infrastructures when the data is not used or using data that hasn’t been maintained?
  • How do new policies shape/discredit/limit access to data that typically guides long-standing policy? (e.g., redefining how poverty is measured, the development of 1970s privacy laws, ramifications of FOIA or “open government”, EPA’s redefinition of scientific “transparency” etc.)
  • What are the social and cultural ramifications of policy choices concerning how data infrastructures are funded, managed, organized, and maintained? (e.g., NASA’s austerity budgeting, procurement and technical outsourcing, creation of OSTP, rules that govern who can lead statistical agencies, etc.)
  • How might defensive strategies (e.g., organizational norms, administrative policies, standards bodies, actions by professional associations, etc.) “check” political power or rebuild trust? (e.g., “Public interest technologists,” new interpretations of administrative or privacy law)

We encourage attendees to approach the Data & Society Workshop series as an opportunity to engage across fields, and to strengthen both relationships and research through participation. While we recognize the value for individual authors, we also see this as a field-building exercise valuable for all involved.


The event will take place online on Thursday, March 25, 2021, currently scheduled from 10 a.m. ET through 5 p.m. ET (exact timing to be confirmed). Participants will be offered a $150 stipend, contingent upon tax laws and acceptance. Unlike a conference, this workshop focuses on reading and offering interdisciplinary responses to in-progress draft papers.

We strongly encourage participation from scholars outside of the United States. That said, we recognize that some constraints may be burdensome to certain participants. This event will be held in English and in the Eastern Standard Timezone.

Authors: this is a fantastic venue for workshopping a paper. If you have an appropriate paper in-progress, you are strongly encouraged to submit it for consideration. Drafts of journal articles, conference papers, law review papers, and book chapters are all welcome. Papers are expected to be in draft stage with room for improvement; the goal of this event is not to present finished work but to truly workshop works-in-progress. Authors will not present their work, but rather listen to critical discussion from the assembled group about the paper, with the explicit intent of making the work stronger and more interdisciplinary. Note that authors are expected to read and provide feedback for 2 other papers during other sessions, in addition to receiving comments on their own work.

Given that this is an academic workshop about public-sector data infrastructure, we expect the papers and discussion to be exceptionally wonky. Please be prepared to include a brief glossary of domain-specific acronyms, laws, or other esoteric details with your paper so that those who work in a different context can go deep with you on the intellectual questions. If you come from an academic tradition where papers are often 100+ pages, please submit a subset of your paper for conversation. Most papers will be between 10-15K words.

Participants: If you do not wish to submit a paper-in-progress but are interested in the topic, we welcome your application as a participant. All workshop participants will be asked to read 3 full papers in advance of the event and to prepare comments for intensive discussion. Some participants will be asked to be discussants of papers, and lead the conversation to engage the group in feedback. Participants should be working in a relevant field, even if they themselves are not offering a paper for consideration.

The workshop will include a mix of deep-dive discussions, networking opportunities, and 3 slots focused on workshopping papers. Each paper session will be 75 minutes long. One paper will be workshopped in each session. Multiple sessions will run in parallel so there will be a total of 9-15 papers, but each participant will only be responsible for participating in 3 sessions. Within each group, a discussant will open with a critique of the paper before inviting participants to contribute responses and suggestions.

All participants will have the opportunity for informal networking and optional 1:1 connections throughout the event. Participants are expected to attend the entire day; authors must participate in all sessions, not only their own. Participants will find that the generous giving of feedback is as intellectually fruitful as receiving feedback.

How to Apply

If you are interested in attending this workshop, you may either 1) propose a paper to be workshopped (author); or 2) describe how your research makes you a relevant discussant/participant.

Please note: All co-authors who are intending to attend must apply separately. They should submit the same paper abstract.

By January 8, please submit the following information here:

  • Name, email address, affiliation, title, discipline.
  • Bio or link to work.
  • If applying as an author, a 1-page (max) abstract of a paper you’d like to workshop.
  • If applying as a participant/discussant, a 1-page (max) discussion of your interests and expertise as it relates to this topic.
  • Your 3 favorite books/papers related to this workshop. [Optional]

Please contact [email protected] with any questions.

Key Dates

All dates listed are by 11:59 p.m. ET

  • Application Deadline                                Friday, January 8, 2021
  • Selection Decisions                                   Monday, January 25, 2021
  • Revised Abstracts + RSVP Deadline    Friday, February 5, 2021
  • Full Paper Deadline                                   Friday, February 20, 2021
  • Workshop (10 a.m. – 5 p.m. ET)             Thursday, March 25, 2021