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| Barocas and Selbst | |
|---|---|
| Name | Barocas and Selbst |
| Author | Suresh Venkatasubramanian; Solon Barocas; Andrew Selbst |
| Subject | Algorithmic fairness; discrimination law; computational social science |
| Publisher | Cambridge University Press; IEEE; ACM |
| Language | English |
| Genre | Law; Technology; Ethics |
Barocas and Selbst
Barocas and Selbst is a widely cited scholarly work addressing algorithmic bias, automated decision-making, and anti-discrimination law. Scholars such as Cathy O'Neil, Kate Crawford, Virginia Eubanks, Frank Pasquale, and Shoshana Zuboff have engaged with its arguments in literature spanning Harvard Law Review, Yale Law Journal, Stanford Law Review, Columbia Law Review, and University of Chicago Law Review. The work intersects debates involving institutions like Equal Employment Opportunity Commission, Federal Trade Commission, European Commission, United Nations, and World Bank.
Solon Barocas, with affiliations including Cornell University and Microsoft Research, and Andrew Selbst, associated with Columbia Law School and New York University, draw on interdisciplinary literatures from Harvard University, Massachusetts Institute of Technology, Stanford University, Princeton University, and University of California, Berkeley. Their scholarship builds on prior work by researchers at Google Research, Facebook AI Research, OpenAI, DeepMind, and policy analyses from ACLU, Electronic Frontier Foundation, Berkman Klein Center, Center for Democracy & Technology, and Data & Society Research Institute. Influences include theoretical foundations from John Rawls, Karl Marx, Michel Foucault, John Stuart Mill, and methodological approaches from Latanya Sweeney, Timnit Gebru, Joy Buolamwini, Hanna Wallach, and Cynthia Dwork.
Barocas and Selbst argue that automated systems reproduce and amplify historical patterns recognizable in cases involving Brown v. Board of Education, Title VII of the Civil Rights Act of 1964, Fair Housing Act, Americans with Disabilities Act, and Voting Rights Act of 1965. They contend that technical fixes developed in forums like NeurIPS, ICML, ACL (conference), KDD, and SIGKDD are insufficient without legal interventions from bodies such as Supreme Court of the United States, European Court of Human Rights, and regulatory proposals from European Union institutions like the General Data Protection Regulation. The thesis synthesizes concepts from statistical parity, equalized odds, predictive parity, and case law including Griggs v. Duke Power Co. to show how algorithmic design choices interact with civil rights regimes.
Their legal analysis examines liability frameworks under doctrines traced to Tort law, precedent from Brown v. Board of Education-era litigation, and enforcement by agencies like Department of Justice and U.S. Equal Employment Opportunity Commission. Ethically, they engage philosophers and ethicists such as Hannah Arendt, Immanuel Kant, Peter Singer, and contemporary ethicists at Oxford University, Harvard Kennedy School, and Princeton University. Technical concepts tied to bias auditing, provenance tracking, and dataset curation reference practices discussed at AAAI, IEEE Symposium on Security and Privacy, USENIX Security Symposium, and research labs at MIT Media Lab. They analyze algorithmic harms in contexts involving healthcare systems impacted by HIPAA, criminal justice systems illuminated by COMPAS controversy, and financial services regulated under Dodd–Frank Wall Street Reform.
Barocas and Selbst recommend regulatory and institutional reforms engaging Congress of the United States, European Parliament, Federal Communications Commission, Office of Management and Budget, and international standards bodies like International Organization for Standardization. Policy tools they discuss include audit mandates modeled on frameworks from National Institute of Standards and Technology, disclosure regimes akin to Freedom of Information Act, impact assessments similar to Environmental Impact Assessment procedures, and enforcement partnerships with state attorneys general and consumer protection agencies. They propose collaboration between academic centers such as Berkman Klein Center, Data Science Institute at Columbia University, and industry initiatives at Partnership on AI.
The work has been cited across disciplines by authors at Harvard Business School, Wharton School, London School of Economics, University of Oxford, and in policy reports from OECD, World Economic Forum, Brookings Institution, RAND Corporation, and Urban Institute. It influenced legislative drafts like those debated in California State Legislature and national consultations in the European Commission leading to proposals related to the AI Act. Judges, including those on the U.S. Court of Appeals for the Second Circuit and commentators at SCOTUSblog, have referenced themes comparable to those in the work. Industry responses emerged from Amazon, Microsoft, IBM, Google, and Facebook research and compliance units.
Critiques from scholars at University of Chicago Law School, Yale Law School, New York University School of Law, and commentators in The Atlantic, New York Times, and The Guardian challenge the feasibility of strict liability regimes and question trade-offs emphasized by economists at National Bureau of Economic Research and policy analysts at Cato Institute and American Enterprise Institute. Debates continue around technical standardization advocated by ISO versus flexible regulatory approaches proposed by OECD and civil-society coalitions including Amnesty International and Human Rights Watch. Academics such as Alexandra Chouldechova and Samsonovich have offered technical counterpoints, while legal scholars including Richard Epstein and Martha Nussbaum have debated normative premises.
Category:Algorithmic fairness