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Solon Barocas is a Senior Principal Researcher in the New York City lab of Microsoft Research, where he is a member of the Fairness, Accountability, Transparency, and Ethics in AI (FATE) research group. 

His research explores ethical and policy issues in artificial intelligence, particularly fairness in machine learning, methods for bringing accountability to automated decision-making, and the privacy implications of inference. He is co-author of the textbook Fairness and Machine Learning: Limitations and Opportunities and he co-founded the ACM conference on Fairness, Accountability, and Transparency (FAccT).
 

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arXiv

The Fair Lending Model

How the Longest-Running Algorithmic Fairness Programs Work in Practice

Solon Barocas and coauthors offer the first empirical account of how financial institutions test for and mitigate algorithmic discrimination on the ground.

Jun 12, 2026
arXiv

Distinguishing Task-Specific and General-Purpose AI in Regulation

Faculty Associates Andrew Selbst and Solon Barocas and coauthors identify ways that general-purpose AI requires regulatory tools not necessitated by task-specific AI.

Jan 23, 2026
University of Pennsylvania Law Review

Unfair Artificial Intelligence: How FTC Intervention Can Overcome the Limitations of Discrimination Law

BKC Faculty Associate Solon Barocas writes about the Federal Trade Commission's intent to regulate discriminatory AI products and services. 

Aug 9, 2022