Solon Barocas

Solon Barocas
Name	Solon Barocas
Occupation	Researcher, Professor
Known for	Fairness in machine learning, algorithmic bias, data ethics
Alma mater	Harvard University, Princeton University

Solon Barocas

Solon Barocas is an American researcher and academic known for work on fairness and ethics in machine learning, algorithmic discrimination, and data-driven decision-making. He has been affiliated with leading institutions in artificial intelligence and law, contributing to interdisciplinary dialogue among scholars at Harvard University, Cornell University, Stanford University, Princeton University, and New York University. His scholarship bridges technical research, public policy, and legal analysis, engaging with communities at Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and the American Bar Association.

Early life and education

Barocas completed undergraduate and graduate studies that combined interests in computer science, public policy, and law. He received degrees from Harvard University where he engaged with programs connected to Harvard Kennedy School and the Harvard John A. Paulson School of Engineering and Applied Sciences. He pursued doctoral work at Princeton University focusing on interdisciplinary methods that drew on computational learning theory associated with groups like the Association for the Advancement of Artificial Intelligence and statistical communities including the American Statistical Association. His early academic mentors included scholars affiliated with Massachusetts Institute of Technology, Yale University, and Columbia University, situating his training at the intersection of technical research and regulatory scholarship influenced by debates at United States Supreme Court levels and policy forums such as OECD and European Commission workshops.

Academic career

Barocas has held faculty and research appointments across departments and centers that span computer science, information studies, and law. He served as faculty in programs connected to Cornell University and its affiliated Cornell Tech campus, collaborating with researchers at Institute for Advanced Study and practitioners from Microsoft Research and Google Research. He has been a principal researcher at interdisciplinary centers collaborating with Berkman Klein Center affiliates, think tanks like Brookings Institution, and advocacy organizations such as the ACLU and Electronic Frontier Foundation. Barocas has contributed to conferences organized by NeurIPS, ICML, and AAAI, and participated in panels with regulators from the Federal Trade Commission and lawmakers on Capitol Hill.

Research and contributions

Barocas's research focuses on fairness, accountability, transparency, and ethics in automated systems. He has advanced concepts that connect formalizations from computer science subfields like computational learning theory, causal inference methodologies associated with Judea Pearl, and statistical parity definitions used in social science research at institutions like RAND Corporation. His work examines how algorithmic systems deployed by entities such as Amazon (company), Facebook, Google, and Uber Technologies can reproduce or amplify inequities identified in studies from Pew Research Center and reports by the United Nations and World Bank. He has collaborated with legal scholars who publish in venues including Harvard Law Review, Yale Law Journal, and Stanford Law Review to translate technical notions of fairness into frameworks suitable for regulatory contexts like the General Data Protection Regulation and U.S. civil rights statutes such as the Civil Rights Act of 1964. Barocas has co-authored influential analyses on algorithmic auditing techniques that draw on methods from statistical learning theory and empirical evaluation used by teams at OpenAI and academic groups in University of California, Berkeley.

Teaching and mentorship

Barocas has taught courses and seminars that bring together students from technical and legal backgrounds, collaborating with programs at Princeton University, Cornell University, and professional schools like Stanford Law School and Harvard Law School. His pedagogy emphasizes hands-on projects linking algorithmic design to policy analysis, and he has supervised graduate theses that connect to research groups at MIT Media Lab, UC Berkeley School of Information, and Columbia University School of Engineering and Applied Science. Through workshops and summer schools coordinated with NeurIPS and the Alan Turing Institute, he has mentored junior researchers and practitioners who later joined organizations such as Microsoft Research, Amazon Web Services, and startups in the Silicon Valley ecosystem.

Awards and honors

Barocas's work has been recognized by awards and fellowships from academic and policy organizations. He has received support from foundations and institutions including the National Science Foundation, the MacArthur Foundation, and research grants tied to collaborations with the Smithsonian Institution and the Carnegie Endowment for International Peace. His scholarship has been cited in policy reports by the European Parliament and in regulatory discussions involving the Federal Communications Commission and the U.S. Department of Justice. He has been invited as a keynote speaker at meetings of the Association for Computing Machinery and pro bono panels hosted by the Open Society Foundations.

Selected publications

- Barocas, S., & Selbst, A. D. "Big Data's Disparate Impact." Published in venues engaging Harvard Law Review style scholarship, intersecting with debates at Yale Law Journal and Stanford Law Review on discrimination law and algorithmic bias. - Barocas, S., Hardt, M., & Narayanan, A. Contributions to textbooks and edited volumes used in curricula at MIT Press and courses at Harvard University and Princeton University on fairness, accountability, and transparency. - Barocas, S., & Dwork, C. Papers presented at NeurIPS and ICML addressing technical definitions of fairness and metrics influenced by research from Judea Pearl and statisticians associated with American Statistical Association. - Barocas, S., Selbst, A. D., & Kroll, J. Collaborations published in interdisciplinary outlets that inform policy dialogues at the European Commission and advocacy at organizations such as the Electronic Frontier Foundation and the ACLU.

Category:Living people Category:Researchers in artificial intelligence Category:Fairness in machine learning