Ethics of Administrative Data Sharing

Ethics of Administrative Data Sharing: Agenda-Setting Framework

2 | Ethics of Administrative Data Sharing: Agenda-Setting Framework

Working Group Signatories

Jeff BehrendsLecturer, Department of Philosophy & Director of Ethics and Technology Initiatives, The Edmond J. Safra Center for Ethics, Harvard University

Khiara M. BridgesProfessor of Law, University of California, Berkeley School of Law

Hanna Kiri GunnAssistant Professor, Department of Cognitive and Information Sciences, University of California, Merced

Yafit Lev-AretzAssistant Professor of Law, Zicklin School of Business, Baruch College, City University of New York

Kay MathiesenAssociate Professor, Department of Philosophy and Religion, Northeastern University

Kobbi NissimMcDevitt Chair in Computer Science, Georgetown University; Affiliate Professor, Georgetown Law; Senior Research Fellow, Center for Research on Computation and Society, Harvard University

Amy O’HaraResearch Professor, Massive Data Institute & Executive Director, Federal Statistical Research Data Center, McCourt School for Public Policy, Georgetown University

Kenneth PrewittCarnegie Professor of Public Affairs, School of International and Public Affairs & Special Adviser to the President, Columbia University

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Amy O’HaraResearch Professor, Massive Data Institute & Executive Director, Federal Statistical Research Data Center, McCourt School for Public Policy, Georgetown University

Kenneth PrewittCarnegie Professor of Public Affairs, School of International and Public Affairs & Special Adviser to the President, Columbia University

Henry S. RichardsonProfessor of Philosophy & Senior Research Scholar, The Kennedy Institute of Ethics, Georgetown University

Micah SherrProvost’s Distinguished Associate Professor, Department of Computer Science, Georgetown University

Lisa SinghProfessor of Computer Science & Research Professor, Massive Data Institute, Georgetown University

Ethics of Administrative Data Sharing: Agenda-Setting Framework | 3

Authors

Elizabeth EdenbergSenior Ethicist & Assistant Research Professor, Ethics Lab, The Kennedy Institute of Ethics, Georgetown University

Mark L. HaninPostdoctoral Fellow, Ethics Lab, The Kennedy Institute of Ethics, Georgetown University

Maggie LittleDirector, Ethics Lab; Senior Research Scholar, The Kennedy Institute of Ethics; Professor of Philosophy, Georgetown University

Working Group Signatories (continued)

Henry S. RichardsonProfessor of Philosophy & Senior Research Scholar, The Kennedy Institute of Ethics, Georgetown University

Micah SherrProvost’s Distinguished Associate Professor, Department of Computer Science, Georgetown University

Lisa SinghProfessor of Computer Science & Research Professor, Massive Data Institute, Georgetown University

This working group was funded by a grant from the Alfred P. Sloan Foundation.Acknowledgments

About Ethics LabGeorgetown’s Ethics Lab is a unique team of philosophers and designers developing new methods to tackle a morally complex world. We bring together deep ethical expertise with creative methods from Design to train students and empower experts to address the most urgent issues of our time — and responsibly shape our future.

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Executive Summary ...................................................................... 6

Statement of the Challenge and Definitions .............................. 8

Working Group Convening......................................................... 10

Key Values for Administrative Data Sharing ............................ 12

Project Evaluation: Responsibilities and Standards ............... 19

Moving Forward .......................................................................... 24

Table of Contents


Administrative data sharing is opening up exciting oppor-tunities for novel social science research, empirically-driven policymaking, and other socially beneficial pursuits. Data sharing can help spur novel medical investigations, aid researchers in identifying the determinants of social mobil-ity, hold governments accountable by ensuring that taxpayer dollars are spent on effective social programs, and develop new tools to address a host of pressing global challenges relating to health, education, migration, climate change, and other core areas of concern.

The success of administrative data sharing depends on tack-ling a range of technical and policy challenges centered on breaking down silos for data access, establishing protocols for data integration, and encouraging intermediaries to fa-cilitate data sharing agreements and projects. But, critically, success will also require attending to the ethical challenges this emerging field brings. These ethical issues are complex, high-stakes, and urgent. If ethical standards lag behind technological and policy progress, the field of administrative data will have trouble building and sustaining the legitima-cy and public trust that it needs to thrive and advance the social good.

To help identify and analyze ethical challenges in admin-istrative data sharing, Georgetown University convened a group of scholars in fields including ethics, technology, law, and policy. The working group met for a day-long workshop in September 2019 with the goal of outlining an ethical framework for administrative data sharing to inform actionable guidelines. The convening was funded by the Alfred P. Sloan Foundation and will contribute to the work of the Administrative Data Research Facilities Network (ADRF).

Executive Summary

If ethical standards lag behind technological and policy progress, the field of administrative data will have trouble building and sustaining the legitimacy and public trust that it needs to thrive and advance the social good.


Executive Summary

The working group’s rich discussions singled out five ethical values integral to administrative data sharing: privacy, consent, social justice, public trust, and transparency. Data sharing implicates a wide range of privacy interests both of individuals and groups that stem from re-identification risks, unexpected inferential insights, as well as data security challenges. These risks must be responsibly managed. While consent is often seen as a way to allay privacy concerns, consent plays a fraught and complex role in data sharing, not least because meaningful consent is often absent for collection of government data. Privacy and consent-relat-ed concerns raise issues of social justice, as well. Historic patterns of bias and discrimination can be reflected in accumulated data, exposing certain communities to greater risks of harms; and communities that have historically had more data collected about them may harbor mistrust of certain institutions in the data sharing ecosystem. Earning high levels of public trust across the board for stewards of administrative data will be critical in the years ahead. One core aspect of trust is robust accountability. Accountability, in turn, requires transparency about the standards and pro-cedures used in data sharing arrangements, as well as about inevitable lapses that occur.

With respect to each of these five ethical values, the work-ing group emphasized two cross-cutting objectives: (1) transcending blanket value concepts to interrogate what they mean in concrete situations; and (2) focusing on an imperative to protect the rights and interests of individuals, as well as communities, implicated by data sharing practices.

The working group also took up questions of responsibility for protecting and advancing the ethical values at stake in data sharing projects. In particular, it addressed how the locus of responsibility for vetting data sharing projects should be allocated among data providers, data interme-diaries, and data requestors. Here, the diverse purposes of data sharing projects emerged as a key theme. The working group grappled with how to define the “common good”

and whether some goals of data sharing projects should be disqualifying altogether. It considered the merits of sub-stantive versus procedural standards to review data sharing proposals; underlined the importance of consulting with diverse stakeholders; and considered the relevance of an Institutional Review Board model in the administrative data sharing context.

Based on these discussions, the group advanced two recom-mendations to guide the development and use of an ethical framework for administrative data sharing. First, ADRF, as well as relevant funders, should support and establish a working group to develop baseline ethical obligations as well as ethical best practices in a process designed to include interdisciplinary participants and the perspectives of a broad range of stakeholders. Second, as these guidelines emerge, any funders of research and development in this space should require that projects meet the emergent ethical guidelines.

Recommendations:1. ADRF, as well as relevant funders,

should support and establish a working group to develop baseline ethical obligations and best practices in a process designed to include interdisciplinary participants and the perspectives of a broad range of stakeholders.

2. As these guidelines emerge, any funders of research and development in this space should require that projects meet the emergent ethical guidelines.


Statement of the Challenge and Definitions

Administrative data sharing is opening up exciting oppor-tunities for novel social science research, empirically-driven policymaking, and other socially beneficial pursuits. Data sharing can help spur novel medical investigations, aid researchers in identifying the determinants of social mobil-ity, hold governments accountable by ensuring that taxpayer dollars are spent on effective social programs, and develop new tools to address a host of pressing global challenges relating to health, education, migration, climate change, and other core areas of concern.

At its most general, administrative data refers to data that are collected by organizations, such as government agencies and businesses, for operational or record-keeping purposes.1 Such data are gathered in service to the organization’s mis-sion or business model, rather than for purposes of scientific or academic research. Examples include data gathered by the Social Security Administration and Internal Revenue Service, various educational records collected by schools and school districts, patient information obtained and stored by healthcare providers and health insurers, as well as location data generated by use of maps and directional services on mobile devices. In administrative data sharing, a requestor gains access to one or more administrative datasets. In order for multiple sets of administrative data to be shared effi-ciently and securely with those requesting access, third-par-ty organizations can play a role in vetting project proposals and facilitating data sharing arrangements by providing technical and logistical support. Such organizations are known as data intermediaries. Their aim is to “facilitate a data sharing relationship between researchers and data pro-viders”2 by, among other things, reviewing project proposals and granting access to requisite data in secure environ-ments. Data providers are entities that have collected and/

Recognizing that serious ethical questions are at stake and committing one’s organization to engaging with them in good faith is a foundational challenge for those involved in data sharing in any capacity. Grappling early on with ethical issues is essential to ensuring responsible data stewardship.


or stored administrative data and who are controlling data. Data providers can include pharmaceutical firms, mobile phone service companies, insurance providers, federal, state, and local government agencies, and other entities. Finally, data requestors are individuals or institutions—for exam-ple, university researchers or private companies—seeking to gain access to data held by data providers for a broad array of uses, including operations and administration, research and evaluation, and compliance reporting, among others.

Administrative data sharing faces a wide range of techni-cal, policy, and ethical challenges. While this white paper focuses on ethical issues, here we briefly highlight key technical and policy challenges that are relevant to the eth-ical analysis of administrative data sharing. These include, among others, (1) siloed datasets held by a vast array of organizations without formalized policies for providing researchers transparent, rule-based access to those data; (2) absence of uniform standards for negotiating data sharing agreements; (3) data integration challenges when bringing together siloed datasets to ensure a principled approach that avoids inaccurate samples or misrepresentation; (4) need for trustworthy intermediaries with sustainable organiza-tional models that can effectively facilitate complex data sharing projects and help ensure high data quality through standardization; and (5) need for authoritative guidelines, guardrails, and best practices for assessing proposed projects along with robust protocols for executing data sharing ar-rangements in secure ways. These and other hurdles must be approached in light of core longer-term trends in data col-lection and curation, in which administrative data collected in daily transactions could complement more traditional methods of data collection for research.

To help tackle these challenges and enable responsible administrative data sharing, the Alfred P. Sloan Foundation supported the establishment of the Administrative Data Research Facilities (ADRF) Network, which was launched

in 2017. ADRF describes itself as “an evolving grassroots effort among researchers and organizations who are seeking to collaborate around improving access to and promot-ing the ethical use of administrative data in social science research.” The Network is focused especially on the subset of administrative data that “are proprietary, restricted access, and/or private, since those [data] present significantly more barriers to access compared to public and open-ac-cess administrative data.”3 The original network consisted of eight institutes or centers focusing on data sharing and analysis housed at six universities—Georgetown University, Stanford University, University of Chicago, University of Michigan, University of Maryland, and University of Penn-sylvania—as well as the Latif Jameel Poverty Action Lab and the Urban Institute.

Administrative data sharing raises complex and urgent ethi-cal challenges, particularly because technology is advancing at a faster pace than ethical standards in this field. Key ethical challenges thus need to be clearly identified, ana-lyzed carefully, and responsibly addressed. Recognizing that serious ethical questions are at stake and committing one’s organization to engaging with them in good faith is a foun-dational challenge for those involved in data sharing in any capacity. Grappling early on with ethical issues is essential to ensuring responsible data stewardship. Simply put, data intermediaries, providers, and requestors must all operate with a moral compass.

Statement of the Challenge and Definitions

Key ethical challenges thus need to be clearly identified, analyzed carefully, and responsibly addressed.


Working Group Convening

As a part of Georgetown’s contribution to the ADRF, Eth-ics Lab—a center for translational ethics at Georgetown—convened a workshop in September 2019 dedicated to investigating the ethics of administrative data sharing across the public and private sectors. This was the first workshop dedicated to this theme under the auspices of the ADRF. It aimed to surface core ethical issues and set the agenda for developing more robust ethical guidelines in the emerging field of administrative data sharing.

The one-day convening brought together an interdisciplin-ary group of scholars from across the United States in a range of complementary fields including ethics, technology, law, and policy. Participants included philosophers work-ing on the ethics of digital technologies, as well as political philosophy; lawyers focusing on technology policy and the intersection of law, race, and class; computer scientists doing pioneering work on differential privacy, data security, and data analytics; and social scientists with deep expertise in data sharing and experience in leadership roles in the U.S. Census Bureau.

Ethics Lab designed the workshop to elicit the diverse perspectives of the experts assembled and place them in productive conversation. Ethics Lab has developed a “translational ethics” methodology that fuses techniques from design with carefully crafted philosophical questions and scenarios to illuminate ethical tensions, encourage fruitful exchanges, and help affect real-world change.4 These methods include making ideas visual—and thus easier to see, sort, and refine—and using fast-paced exercises that foster an atmosphere of group problem-solving and exper-imentation. A collaborative, interdisciplinary approach is particularly important to chart responsible paths forward

The one-day convening brought together an interdisciplinary group of scholars from across the United States in a range of complementary fields including ethics, technology, law, and policy.


for complex and rapidly evolving ethical challenges. For this workshop, Ethics Lab created activities that drew on the expertise of each discipline while building bridges to yield emergent insights.

The morning session was intended to bring to light how each participant sees the core ethical issues at stake in data sharing. Using individual response cards that were posted for the group’s perusal, participants identified bold ideas for data sharing, values that tend to be over- or under-em-phasized, and stories related to data sharing that serve as inspiring or cautionary tales. This brainstorming allowed each participant to convey their ideas and reflect on those of others. This process set up an in-depth facilitated con-versation throughout the day on foundational ethical issues confronting administrative data sharing.

The afternoon session began with two case studies, each focused on a distinct ethical issue. Participants worked in rotating mixed-discipline groups. The first case explored the rights and interests of individuals whose data are being shared and whose rights and interests are implicated by data sharing yet are at risk of being overlooked. The case study

asked participants to evaluate a hypothetical data sharing project from the perspective of various interest-bearers in order to discern their priorities, consider how these priori-ties might change over time, and brainstorm ways to protect their interests.

A second case study explored the purposes of data sharing projects by contrasting similar requests from entities with different missions and goals. This case study encouraged discussion about the contested notion of the “common good” or “public good” in relation to data sharing. It helped the group identify factors that would be important in de-veloping best practices for intermediaries to vet requests for data access.

The workshop concluded with an exercise designed to generate a discussion that identified and distilled key take-aways from the workshop to inform this white paper. The conversation surfaced commitments that achieved broad consensus as well as complexities that elicited a plurality of views and merit further work. This white paper reflects the insights that emerged from the convening.

Working Group Convening

Participants included philosophers working on the ethics of digital technologies, as well as political philosophy; lawyers focusing on technology policy and the intersection of law, race, and class; computer scientists doing pioneering work on differential privacy, data security, and data analytics; and social scientists with deep expertise in data sharing and experience in leadership roles in the U.S. Census Bureau.


Certain core values and themes emerged from our discus-sion. We begin with identifying each one and highlighting salient considerations raised for administrative data sharing. Many of these concepts are complex and contested, and participants brought a broad range of views to the work-shop. But efforts at understanding and addressing ethical stakes in the field of administrative data sharing should be anchored in careful scrutiny of the following key values.

PrivacyAdministrative data sharing raises distinctive challenges for a broad range of privacy interests. This section highlights some of these challenges. One overarching lesson that emerged in the workshop is that the set of parties whose privacy inter-ests are implicated is broader and more diverse than it may initially seem. We first discuss the privacy interests of indi-viduals whose data are included in the datasets flagged for sharing. These data often reflect personal information that individuals have a strong interest in protecting from unau-thorized access. We then address parties whose information is not captured in administrative datasets but who may have genuine privacy interests at play when the data analytics tools developed based on those datasets reveal inferences about them on sensitive topics.

Before proceeding, it is important to distinguish among three related concepts—privacy, confidentiality, and security. Pri-vacy can be thought of as broadly related to the interests that individuals or groups have in protecting certain information about themselves; confidentiality as referring to obligations to safeguard others’ privacy interests; and security as one di-mension of confidentiality that, among other things, requires minimizing risk of unauthorized access to relevant data.

Key Values for Administrative Data Sharing

5 Integral Ethical Values: • Privacy • Consent • Social Justice • Public Trust • Transparency


Individuals whose information is contained in datasets earmarked for sharing will have a range of privacy and con-fidentiality interests at stake. First, perhaps most obviously, they have an interest in ensuring the integrity and security of data in ways that protect it from unauthorized access and use. Data requesters who have been authorized to access relevant data also have obligations of confidentiality with respect to personal information entrusted to their care. In general, data providers, intermediaries, and requestors must ensure the security of their data systems in order to protect the private information contained therein. Guar-anteeing the confidentiality and security of data is thus a fundamental obligation of all data stewards. This includes ensuring that data storage and management practices meet high standards that minimize risks of unauthorized access. Disclosure risks must be proactively identified and compre-hensively mitigated when large and complex datasets are transferred across organizations.

Second, individuals have an interest in ensuring the confi-dentiality of information about them that is contained in the dataset slated for sharing. Data providers may initially seek to protect individuals’ privacy by ensuring that relevant information in datasets is de-identified or anonymous. That is, technical strategies may strive to decouple an individual’s identity from the data points about that individual. But iron-clad anonymization is typically an illusory goal. Risks of re-identification are pervasive and must always be care-fully considered. Even if the data in each particular dataset are anonymized, the privacy risks to the individual may increase as multiple datasets are linked. Once a researcher gains access to multiple datasets, if the data can be scruti-nized using powerful analytic tools and cross-referenced with other publicly available information, resulting analyses can increase the chances that an individual may be re-iden-

tified. The more sensitive the underlying data—for example, if it concerns health or financial records—the stronger the privacy and confidentiality interests at stake will be.

Data stewards must therefore consider strategies to evaluate and mitigate re-identification risks. In particular, knowing which data a requestor already possesses, and may later gain access to, is essential to assess the chances of re-iden-tification and the probability that disclosure limitation techniques can reduce those risks to acceptable levels. Yet there are hurdles to obtaining such information. Requestors may be unwilling or unable to share it; or they might have too much information to convey and not know which is relevant. And sharing such information may itself surface stand-alone privacy concerns. In short, analyses of the pri-vacy risks posed by data sharing are both critical and likely to be incomplete.

Third, the privacy interests of individuals included in the dataset also extend to other inferences that may emerge from analyses of the data. A key objective of administrative data sharing, after all, is to link datasets and analyze them to extract valuable insights that would not otherwise be known. But these very insights open the door to privacy risks that can be hard to anticipate and counteract. For example, a data sharing project to determine the penetrance and severity of opioid addiction in certain neighborhoods or workplaces may link together data about individuals’ employment history, health records, prescriptions, and de-mographic data.5 Inferences of this general kind, combined with re-identification risks, illustrate the privacy interests at stake for data subjects.

Such risks extend more broadly, as well. The generalizabil-ity of inferential analyses poses privacy risks to individuals whose personally identifiable data are not contained in datasets meant for sharing. For example, if modeling reveals that individuals with certain characteristics living in defined areas have a high probability of having opioid addiction, this may implicate the privacy interests even of those whose data were not included in the data sharing project. Re-


Privacy can be thought of as broadly related to the interests that individuals or groups have in protecting certain information about themselves.


searchers might be able to draw inferences about the likely health status of individuals whose data is not included in the dataset and who may legitimately view their risk for illness to be private information.

The inferential power of data opens up challenges for data sharing that call for humility and vigilance together with creative solutions. Data intermediaries and data providers should pay close attention to the fallibility of gauging priva-cy risks, including re-identification risks, and develop re-sponsible mitigation strategies. And they should be attuned to the interests of individuals and groups not contained in the datasets but whose privacy may well be at stake given the inferential power of data.

ConsentConsent is often seen as a strategy to allay privacy concerns. Yet in the data sharing context, consent raises a host of subtle considerations at different stages of a data sharing project, beginning with consent to the initial collection of data, consent to sharing data, and consent to the use of data in the proposed research.

First, the standards for legitimate consent to data collection vary according to the type of entity collecting the data. In the commercial sphere, consent to data collection is gen-

erally presented through terms of service and/or privacy policies that an individual ‘agrees’ to in order to access the service or is presumed to ‘agree’ to by remaining on the site or platform. It is frequently assumed that by volun-tarily using such a service, an individual has consented to all the terms outlined by that entity—terms which may include the possibility of data harvesting and future sharing of identified or de-identified data. Yet existing practices of data collection in these contexts routinely fall short of the standards for specific disclosure and clear itemization of potential risks that are often regarded as prerequisites for meaningfully assuming those risks. Furthermore, few people actually read the terms of service or privacy policies in full. These challenges raise questions about the extent to which ‘agreeing’ to these policies should qualify as valid consent. The implication is that some commercial data used in administrative data sharing projects may not have been obtained with meaningful consent in the first place.

In some non-commercial contexts, consent is not seen as necessary for administrative data gathering to be legiti-mate. When government data are at issue—such as Social Security Administration data or IRS data—these data are acquired in the ordinary course of government procedures without a meaningful option to opt out. Yet such data are still regarded by many as legitimately gathered to fulfill a necessary government service. In this case, the legitimacy of the data collection is tied to its purpose and more general authorization of legitimate government rather than the spe-cific consent of the individual. Secondary uses of govern-ment administrative data for research and evaluation may be permitted through statute, regulation, or policy.


Consent raises a host of subtle considerations at different stages of a data sharing project, beginning with consent to the initial collection of the data, consent to sharing the data, and consent to the use of the data in the proposed research.


Second, aside from questions about the role of consent in data collection, there are further challenges in thinking through whether and when the consent of data subjects is needed to authorize the use of their data for a given data-sharing project. Such uses are rarely anticipated or declared at the time of data collection, rendering it chal-lenging to rely on models of explicit and specific consent to data collection as a tool for protecting data subjects when sharing that data. Further, it may be impractical or impossi-ble to try to secure consent to a specific data sharing project from thousands or millions of individuals whose data are contained in relevant datasets, even when that project has already been outlined. In addition, informed consent is impossible to obtain from as-yet unknown individuals whose data are not contained in relevant datasets but whose privacy interests may be put at risk by re-identification and inferential analyses. Even if obtaining consent to data sharing is technically feasible, if certain individuals opt out, the resulting balkanized dataset may no longer be suitable to advance the relevant research aims of the data requestor. This raises complex questions about how to balance the data subject’s rights with the anticipated society-wide benefits of the proposed research.

Given these challenges, developing appropriately robust consent standards, while clearly articulating when and why consent is required, unnecessary, or permissibly waived or forfeited, is an important area for future research.

Social JusticeEthical issues extend beyond privacy and consent. As we outlined above, data collected about particular individuals can also impact those who either share certain traits with data subjects or may otherwise be impacted by the resulting analyses. Datasets do not exist in a vacuum—the collection, analysis, and implementation of any research or practical intervention must be understood in light of its broader so-cial contexts. While there are a number of distinct consider-ations here, we will highlight two.

First, bias and discrimination surface an important set of concerns. Patterns of social inequality that have complex and long-standing historical roots can often be reflected in the data. Indeed, biases can be introduced right at the moment of data collection. For example, even when race or income are explicitly excluded from datasets, other data points such as zip codes can frequently serve as proxies for these criteria. Similarly, collecting data on encounters with the police will have predictably different results depend-ing on whether one lives in an area subject to high rates of policing. Beyond biases that are reflected in the input data, discrimination can result even from accurate and repre-sentative data if researchers are not careful to consider the impact of data analyses. Furthermore, any analysis must be attuned to the impact of the research, as there can be real harm to individuals when a data sharing project reproduces or reinforces existing patterns of discrimination. Since data or analyses marred by biases and prejudices can reinforce existing patterns of injustice, it is important to be attuned to issues of social inequality that could arise in the collec-tion, analysis, and results of the data sharing project and aim to mitigate them when reviewing and facilitating data sharing projects.

Second, concerns about excessive surveillance stemming from data collection need to be borne in mind. In particu-lar, the working group discussed the ramifications of data sharing projects for historically disadvantaged and mar-ginalized groups. More data has historically been gathered



about certain disadvantaged populations than others. For example, when new mothers request healthcare support from the government, applicants are subject to far more in-trusive questioning and data collection than their wealthier counterparts with private insurance. To the extent that data sharing projects implicate information about less advan-taged groups—and to the degree that enthusiasm for data sharing results in greater data collection on such groups generally—they may face still heightened surveillance and privacy risks while lacking adequate resources to counteract those risks. This heightened surveillance can do real harm to individuals and communities at stake—and these harms can extend beyond violation of individuals’ privacy to im-pact their freedom to pursue their own goals and projects, which may occur, for example, if the fruits of a data sharing project contribute to predictive policing practices that raise ethical red flags.

In sum, administrative data sharing and data collection can impact various communities in disparate, unobvious, and consequential ways that need to be better understood and taken into account to mitigate relevant risks. Attention to justice-based concerns is a key pillar of acceptable da-ta-sharing arrangements.

Public TrustPublic trust in administrative data sharing, a relatively new field, is not foreordained. It is slowly won and easily lost. For the field to grow and thrive, there needs to be meaning-ful, earned, trust by millions of individuals and communities whose interests are implicated by data sharing. Relatedly, data providers, requesters, and intermediaries must also show themselves to be trustworthy through their commit-ment to upholding high ethical standards.

Relative levels of trust in major social institutions—includ-ing government entities and corporations—can vary across communities. Some may see the government as a primary threat to personal liberty and privacy while treating private companies as more trustworthy. Others may worry more about the accountability of private corporations that are driven by the interests of their shareholders rather than the public good. Current and historical relations between the government and different communities can also affect assessments of its relative trustworthiness. For example, some communities experience heightened surveillance and interference by the state as a consequence of historical pat-terns of over-policing and disproportionate rates of arrest and incarceration relative to the general population. Oth-ers may mistrust shifting governmental policies and data collection practices on sensitive political issues. Keeping in

Administrative data sharing and data collection can impact various communities in disparate, unobvious, and consequential ways that need to be better understood and taken into account to mitigate relevant risks.

For the field to grow and thrive, there needs to be meaningful, earned, trust by millions of individuals and commu-nities whose interests are implicated by data sharing.



mind the possibility that data sharing projects can exacer-bate inequality, and steering clear of blanket assumptions about which data stewards enjoy relatively greater public trust than others, it is critical that those involved regard themselves as stewards of public trust, not just data security.

TransparencyNo matter which rules and procedures organizations use to evaluate, approve, and facilitate administrative data sharing projects, these rules and procedures must be comprehensive, clear, and easily accessible to the public. This transparency norm promotes multiple values. First, it helps show due respect for individuals whose data are at issue so that they, or organizations advocating on their behalf, can grasp and assess the potential implications of data sharing projects and, where appropriate, have a fair opportunity to voice concerns. Understanding what is at stake is a precursor for any truly informed consent to research and data shar-ing. Second, and relatedly, accountability is predicated on openness. Meaningful audits and enforcement require full knowledge of the rules and procedures by which an orga-nization operates. Both of the preceding points contribute to a third—developing and sustaining public trust requires transparency about the rules of the road, as well as inevita-ble shortcomings and lapses in the contents and applica-tion of those rules. Transparency alone will not guarantee public trust. But it remains critical so that the public can be informed and hold parties accountable for protecting the rights and interests of individuals and communities as data sharing initiatives proliferate.


Transparency alone will not guarantee public trust. But it remains critical so that the public can be informed and hold parties accountable for protecting the rights and interests of individuals and communities as data sharing initiatives proliferate.


Granularity

The core ethical values that emerged as central to administrative data sharing throughout the convening are varied and rich, and the working group discussed them at multiple levels of abstraction. Terms like “pri-vacy,” “transparency,” and “accountability” are com-plex notions with a broad range of interpretations and connotations. In developing guidelines, guardrails, and best practices for data sharing, it will be essential to transcend these blanket value concepts. Doing so requires untangling their conceptual complexities and asking what those concepts mean in concrete situa-tions. Drafters should challenge themselves to make explicit how they understand those concepts in light of their organization’s mission and relevant social contexts for data sharing. Through this reflective process, core values—along with gray areas, nuances, complications, and points of contestation—will emerge more clearly, encourage debate and refinement, and help produce better and more durable norms for administrative data sharing.

Focus on Data Subjects and Communities

An overarching theme that animated the discussion was the imperative to protect the rights and interests of individuals—as well as communities implicated by data sharing. Paraphrasing one workshop participant, “when we’re talking about data, we’re actually talking about people.” In other words, people’s interests in privacy and dignity—among other values including equality and freedom—are implicated by any data sharing project, no matter how remote or abstract those interests may appear when data sharing arrangements are negotiat-ed and carried out. Granted, there are intriguing and important questions here as to when data in de-iden-tified form cease to be about a particular person, even despite re-identification risks. Nevertheless, adopting the perspective of individuals whose data will be shared provides a basic ethical starting point for accurately cataloguing data subjects’ interests and appropriately protecting them—which may require refusing to approve certain projects or imposing mitigation measures to curb privacy risks or address other concerns.


Overarching themesIn addition to discussion of individual key values, two overarching themes emerged from the day’s discussion: the importance of explicating the above values in granular terms, and the importance of focusing on data subjects and affected communities.


Project Evaluation: Responsibilities and Standards

Whose responsibility is it to protect and advance the ethical values at stake in data sharing projects? And what standards should be used by the responsible parties? These two questions emerged as leitmotifs of the convening.

Data intermediaries and data providers cannot be compla-cent about facing ethical challenges that emerge alongside technical, legal, and policy hurdles. Crafting policies and best practices to address the complex ethical challenges posed by administrative data sharing is a top priority in this growing field. Devoting sufficient time and resources to think through ethical issues relevant to one’s organization and reflecting ethical priorities in a set of practices and procedures is essential to handle ethically sensitive situa-tions that will inevitably emerge and to build trust in data sharing institutions and practices.

Loci of Responsibility The working group considered how the locus of responsi-bility for vetting data sharing projects should be allocated among data providers, data intermediaries, and data re-questors. While all three actors will have some role to play, clarifying those roles and examining which types of entities should be entrusted to serve as data repositories and to be responsible for assessing data sharing requests remains a critical open question. In addition, the process of assessing projects should be designed to take account of the interests of the people whose data are being shared and who may be affected by a data sharing project. These interests may

not be readily known to data providers, intermediaries, and those requesting access to the data, creating a need for other actors or processes to ensure those interests are adequately represented.

While the working group largely focused on vetting criteria for data intermediaries, the responsibilities and prerogatives of data providers call for greater scrutiny. The criteria that data providers use to decide when to make data avail-able—and how those criteria should vary given a provider’s identity—are key topics of concern. When it comes to government agencies in particular, there will be inevitable tension between the anticipated benefits of sharing data and the risks arising from sharing highly sensitive data (even if de-identified in certain respects). Some participants wondered whether legal restrictions should govern the types of data that may be shared under various circumstances and whether data held by government entities merit especially tight restrictions.

Standards and Processes for Project EvaluationDiscussion of substantive and procedural standards for vetting data sharing projects emerged as a key theme of the convening. The working group addressed a host of issues under this heading, including what guidelines should inform data sharing projects, how stringent those standards should be, whether procedural or substantive norms should be prioritized, and what norms of either stripe might look like. Each of these issues raises distinct complexities.


Legal versus Ethical StandardsOne issue is how ambitious best practices should be and how much leeway reviewers of data sharing proposals should have in evaluating projects. In considering a range of norms and procedures, organizations should keep in mind a basic guiding principle: complying with both legal and ethical standards is critical, but they should be clearly dis-tinguished. Adherence to the law may be ethically required, but it is not a sufficient condition for discharging one’s eth-ical responsibilities to individuals and communities affected by data sharing projects. Whereas legal guidelines may set a minimum threshold for compliance, ethical guidelines can set a higher standard. Given the law’s special connection to punishment and sanctions, as well as distinctive challenges of effective enforcement, legal standards often establish a lower bar for compliance than what is expected of peo-ple and organizations in living up to ethical standards. For example, while it may be lawful for intermediaries to facilitate data sharing projects with for-profit objectives, doing so may not advance the common good in all cases and so would not live up to the ideals that intermediaries may aspire to. As a baseline, therefore, best practices need to identify core minimum requirements and guardrails which may in the future become legal norms. But the ethical bar should aim beyond what the law requires.

Although the working group did not discuss legal frame-works for regulating data sharing in detail, the question of an appropriate role for federal regulation of administrative data sharing practices was broached and remained an open question. Some of the norms and standards discussed below could in principle be embodied in relevant laws or regula-tions. In addition, whether data intermediaries should be regulated by the federal government—and if so, how—re-mains an important topic for discussion.

Data Sharing for the “Common Good”The purposes of data sharing projects emerged as a key topic of discussion. How should the objectives of specific projects factor into their evaluation? Which aims deserve more searching scrutiny than others? Should certain pur-poses be altogether disqualifying and off limits from the perspective of ethically responsible intermediaries? If so, which ones? These questions merit special attention in light of the ADRF network’s mission to advance “ethical use of administrative data in social science research.”6

These questions surfaced contestation about the “common good.” What does the “common good” mean in various situations pertinent to data sharing? Should administrative data sharing projects be limited to those that can credibly be said to advance the “common good”? These are un-der-explored topics deserving careful analysis in crafting ethical best practices for intermediaries and other actors.

Simply because a project satisfies criteria of informed consent and other standards of data integrity and technical security does not mean that it should go ahead. Not all re-search objectives are well-founded and socially responsible. Research projects may use dubious methodological ap-proaches and generate ethically problematic, albeit unin-tended, consequences.

An example discussed by the working group highlights the nuances and tensions that can arise in considering how to interpret the “common good” and its relationship to ad-ministrative data sharing. Participants debated whether a university should collect certain student data to look for signs of mental distress and intervene in appropriate cases. Some viewed this objective as a defensible and laudable use of administrative data, despite inevitable privacy concerns to students, and, perhaps others related to them (depending on the information collected). Others saw the proposed in-tervention more skeptically as a form of paternalistic over-reach that may not really be in the public interest. While not a paradigmatic instance of administrative data sharing,


Whereas legal guidelines may set a minimum threshold for compliance, ethical guidelines can set a higher standard.


this example led the working group to interrogate the limits of what counts as being in the “public interest” and whose perspectives are relevant to making this determination—in-cluding those of students, friends, families, administrators, and faculty. In short, negotiating the boundaries of the “common good” will be a complex, albeit inevitable, part of working out comprehensive and long-lasting ethical stan-dards for evaluating data sharing proposals.

Participants also grappled with whether a distinction between projects oriented toward the “common good” or “public good” and projects oriented toward profit-driven ends is illuminating in this context. While views varied, it seemed important not to rush to judgment in either direc-tion. On the one hand, some for-profit projects may have objectives that are partly in the public interest, such as well-run clinical trials for new drugs and treatments. On the other hand, just because a project is not for-profit hardly guarantees that it is for the common good. Relatedly, sim-ply because requestors work at non-profit institutions does not mean that their projects are in the public interest and free of conflicts of interest. Recent cases of political oppo-sition research illustrate that nonprofits can have objectives that run contrary to the common good. In addition, some-times the extent or types of data sought to be collected and linked exceed what would plausibly be needed to achieve the project’s objectives. Or, perhaps, privacy risks entailed by the project (for example, re-identification risks) are alarmingly high, even if executed as designed. In sum, when weighing whether a project furthers the “common good,” taking account of a data requestor’s institutional affiliation may be relevant but is too coarse-grained as a stand-alone criterion. The focus should likely be on the purposes of

specific projects, leaving open hard questions about how to give content to the idea of the “common good” in ways that impose meaningful ethical constraints on data sharing proj-ects while stewarding a broad range of innovative proposals.

Substantive versus Procedural StandardsThe working group considered the appropriate balance between substantive ethical guidelines and procedural safe-guards. Participants raised the idea that a blended model of substantive and procedural guidelines can ensure that proj-ects live up to high ethical standards. Procedural safeguards can play important gatekeeping functions because even well-intentioned projects can raise red flags for a variety of what may loosely be called ‘procedural’ grounds. Among procedural considerations that merit attention in project re-view, some participants pointed to (1) the scientific rigor of data analysis techniques; (2) data storage and management practices of intermediaries, data holders, and requestors; (3) the provenance of the data earmarked for sharing (i.e., where the data were acquired and whether they were legit-imately gathered), which one participant suggested should be part of a dataset’s metadata as it changes hands; and (4) whether requested data could be misused or overused in a given context. As one workshop participant remarked, “there is a science to just learning how to use data.” These and related criteria deserve careful scrutiny.7 It can be easy to promise but hard to ensure that appropriate expertise and technical tools live up to commitments about data privacy and security.

Broad Stakeholder Consultation No matter which entity is playing a role in reviewing and stewarding data sharing projects, consultation with a wide range of stakeholders is a central tenet of procedural legit-imacy. The ADRF has rightly observed that “[e]ngaging diverse stakeholders early and often—and providing them meaningful opportunities for input—can establish lasting trust, create legitimacy in administrative data research, and


In short, negotiating the boundaries of the “common good” will be a complex, albeit inevitable, part of working out comprehensive and long-lasting ethical standards for evaluating data sharing proposals.


help researchers get ahead of privacy disasters.”8 In addition to the concerns already raised, robust input from a broad array of stakeholders is essential for developing ethical guidance that protects people’s interests and devising standards that work for a broad public’s good. The working group surfaced a number of complex issues about how best to consult various stakeholders and when their perspectives should be included in the process of conceptualizing and drafting best practices for administrative data sharing.

Data sharing projects will involve inevitable tradeoffs between the potential benefits of novel research and the interests of individuals and communities whose data are being shared and who may be affected by data sharing projects. It is essential to be clear-eyed about these tradeoffs both when formulating best practices and evaluating spe-cific projects. Different stakeholders may have competing perspectives about what qualifies as a benefit to society and how social benefits should be balanced against individual rights. Furthermore, the rights of marginalized individuals are more frequently overridden by appeals to the public good than the rights of individuals with the resources and political capital to demand and implement better protec-tions. Particular caution should be paid to these differences in social clout to ensure that projects aimed at the “common good” do not reinforce sacrifices of some on behalf of good outcomes for others.

Input from a wide range of perspectives is essential for thinking through the foundational ethical values that should inform administrative data sharing and for devel-oping specific best practices to guide the field. Interdis-ciplinary subject-matter experts and representatives of

affected communities—especially vulnerable populations and racial and ethnic minorities—should be included in developing guidelines and best practices. Robust input from such heterogeneous viewpoints is necessary to ensure that the guidelines developed are appropriately reflective of the interests and concerns of individuals and communities affected by data sharing initiatives, with particular attention to the ways communities may be impacted differently.

Institutional Review Board Model In reflecting on mechanisms and structures that data inter-mediaries could use for project review, the working group discussed the analogy of an Institutional Review Board (IRB) used in universities and other organizations to scruti-nize and approve human subjects research. Could the model of an IRB-like standing committee with clear guidelines be adapted to the intermediary context? IRB review establish-es a procedure for vetting research projects against substan-tive ethical standards for responsible research that protects individual research subjects. A number of participants sug-gested the potential to use IRBs as a model for establishing a blend of substantive and procedural protections that can help ensure ethical administrative data sharing practices.

Participants expressed different views on the rigor of standards IRBs ordinarily used and their utility for data sharing projects. While some participants thought an IRB model suggests relatively robust standards, others voiced skepticism because, in their view, criteria typically used by existing IRBs (at universities, for example) may not be fully attuned to the distinctive ethical quandaries arising in relation to big data analytics, including serious but hard-to-discern privacy risks that come with sharing even de-iden-tified data. Developing relevant expertise to understand and assess the impact of data science research on people and society would thus be important to making an IRB model viable. In sum, the IRB model—both with respect to or-ganizational structure and substantive standards—calls for further exploration to determine whether and how it could be adapted for the administrative data sharing context.


Interdisciplinary subject-matter experts and representatives of affected communities—especially vulnerable populations and racial and ethnic minorities—should be included in developing guidelines and best practices.



Learning from Other FieldsFinally, the working group surfaced another way in which looking beyond the context of administrative data can help inform norms and practices in this field. Although the ethi-cal, legal, and institutional challenges outlined in this white paper raise a host of distinctive concerns for administrative data sharing, these challenges also involve ethical dilemmas common across numerous fields, such as questions of pri-vacy, justice, and trust, as discussed in the previous Section. In deciding how to tackle these challenges for data sharing, there is no need to reinvent the wheel. Looking to other fields—such as the landmark 1978 Belmont Report that set the ethical gold standard for human subjects research or best practices for establishing public trust in large scale public health initiatives—can be useful to generate ideas and to serve as a reference point for evaluating novel pro-posals in the field of administrative data sharing. Well-de-veloped ethical principles, processes, and guidelines from other fields (such as medical ethics) can help inform best practices for data sharing.

There is no need to reinvent the wheel... Well-developed ethical principles, processes, and guidelines from other fields (such as medical ethics) can help inform best practices for data sharing.


The ethical issues confronting administrative data sharing are complex and high-stakes. They are also urgent, given that technology is advancing quicker than ethical standards. Specific ethical guidance is urgently needed for actors in this field, including data intermediaries. Such guidance can take the form of minimal ethical standards, red lines or guardrails, as well as best practices. And it should be informed by the considerations surfaced by this working group.

We are at the early stages of establishing standards and protocols for data sharing. These early stages carry risks. Chief among them is a risk that a focus on overcoming technological barriers may outpace ethical reflection. Yet allowing ethics to lag behind technological and policy inno-vation can put individuals’ interests at risk and weaken the trustworthiness of institutions. These early stages also invite many opportunities. In particular, organizations can commit to addressing ethical challenges not as an afterthought but as an integral part of their missions.

We therefore recommend that ADRF, as well as relevant funders, should support and establish a working group to develop baseline ethical obligations as well as ethical best practices in a process designed to include interdisciplinary participants and the perspectives of a broad range of stake-holders.

Assembling an interdisciplinary group of scholars and providing it ample time and a suitable institutional frame-work to formulate actionable best practices for the ethics of administrative data sharing should be a major priority for the near term. Inviting voices from multiple disci-plines to inform and illuminate each other is generative

Moving Forward

We therefore recommend that ADRF, as well as relevant funders, should support and establish a working group to develop baseline ethical obligations as well as ethical best practices in a process designed to include interdisciplinary participants and the perspectives of a broad range of stakeholders.

We further recommend that—as these guidelines emerge—any funders of research and development in this space should require that projects meet the emergent ethical guidelines.


Moving Forward

and rewarding. Computer scientists, philosophers, lawyers, social scientists, and policymakers should be in continu-al conversation to refine the ethical stakes and take steps toward realistic guidelines. Our working group showed the viability and importance of an interdisciplinary approach to developing ethical guidance and confronting myriad challenges brought to light in our convening in constructive ways. Equally important, this process should also include opportunities for direct input from representative commu-nities whose interests are at stake. This is important both for reasons of basic fairness and because sustaining public trust is integral to the credibility of administrative data sharing. Allowing ample time for an interdisciplinary and inclusive process to design granular recommendations for the ethics of data sharing is thus a key priority.

We further recommend that—as these guidelines emerge—any funders of research and development in this space should require that projects meet the emergent ethical guidelines.

The expectations of funders and donors can powerfully shape incentives to commit to high ethical standards for data sharing projects. For this reason, organizations like the Sloan Foundation should consider including as a condi-tion of funding adherence to, and accountability for, robust ethical standards—for example, the standards developed by the working group envisioned in our first recommenda-tion. Applicants would pledge to abide by ethical standards approved by the funding body, or suitably modified versions thereof. Incorporating such expectations into funding calls would send a clear and welcome message about funders’ pri-orities in this rapidly developing field. Funders and donors can thus play trend-setting and catalyzing roles in encour-aging adoption and refinement of the gold standard in the ethics of data sharing.

In summary, administrative data sharing opens up exciting avenues for research and evidence-driven policymaking. It holds great promise to deal with pressing social chal-lenges, from understanding determinants of inequality to

designing new medical studies, channeling taxpayer dollars to successful programs, and countless other initiatives. For data sharing to flourish, problems of siloed data, ad hoc sharing arrangements, misaligned technical standards, and other issues must be resolved. But in working out technical and policy hurdles, the ethical challenges entailed by data sharing must remain center stage. Dealing responsibly with ethical complexities requires an institutional commitment to robust best practices that go beyond buzzwords, deal openly and wisely with inevitable tradeoffs, and solicit input from a broad set of stakeholders. Making ethics a priority is essential to respect the rights and interests of people whose data make data sharing projects possible. It is also vital to sustain public trust in legitimate institutions and processes so that the benefits of administrative data sharing can bear fruit well into the future.

Dealing responsibly with ethical complexities requires an institutional commitment to robust best practices that go beyond buzzwords, deal openly and wisely with inevitable tradeoffs, and solicit input from a broad set of stakeholders.


1. “Data Sharing Governance and Management,” ADRF Working Paper ( June 2018), p. 4. https://repository.upenn.edu/cgi/viewcontent.cgi?article=1002&context=ad-mindata_reports

2. Id., p. 4.

3. Id.

4. Maggie Little, et al., “Ethics Lab: Harnessing design methodologies for translational ethics,” in A Guide to Field Philosophy: Case Studies and Practi-cal Strategies, edited by E. Brister and R. Frodeman, pp. 66-79 (New York: Routledge, 2020).

5. For discussion of a related example, see Robert M. Groves and Adam Neufeld, “Accelerating the Sharing of Data Across Sectors to Advance the Common Good” (2017), pp. 4, 18-19. https://repository.library.georgetown.edu/bitstream/handle/10822/1051504/Accelerating-Sharing-Data_BeeckCenter.pdf?sequence=1&isAllowed=y

6. ADRF homepage, https://www.adrf.upenn.edu/.

7. For discussion on data quality standards in administrative data sharing, see “Data Quality and Standards,” ADRF Working Paper ( June 2018). https://repository.upenn.edu/cgi/viewcontent.cgi?article=1001&context=ad-mindata_reports

8. “Communicating about Data Privacy and Security,” ADRF Working Paper ( June 2018), p. 4. https://repository.upenn.edu/cgi/viewcontent.cgi?article=1003&context=ad-mindata_reports

End Notes

https://repository.upenn.edu/cgi/viewcontent.cgi?article=1002&context=admindata_reports


https://repository.library.georgetown.edu/bitstream/handle/10822/1051504/Accelerating-Sharing-Data_BeeckCenter.pdf?sequence=1&isAllowed=y

https://repository.library.georgetown.edu/bitstream/handle/10822/1051504/Accelerating-Sharing-Data_BeeckCenter.pdf?sequence=1&isAllowed=y





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Ethics of Administrative Data Sharing

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