Solon Barocas / Regulation By Explanation / 10.03.17

Solon Barocas is an assistant professor in the Department of Information Science at Cornell University. His current 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.

In 2014, he co-founded Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), an annual event that brings together an emerging community of researchers working on these issues. Barocas was previously a postdoctoral researcher at Microsoft Research, where he worked with the Fairness, Accountability, Transparency, and Ethics in AI group, as well as a Postdoctoral Research Associate at the Center for Information Technology Policy at Princeton University.

Barocas completed his doctorate in the Department of Media, Culture, and Communication at New York University, where he remains a visiting scholar at the Center for Urban Science + Progress and an affiliate of the Information Law Institute.

Talk: “Regulation By Explanation”

Abstract: From scholars seeking to unlock the black box to regulations requiring meaningful information about the logic of automated decisions, recent discussions of machine learning have turned toward a call for explanation. Champions of explanation charge that algorithms must reveal their basis for decision-making and account for their determinations. But so far, these calls lack a rigorous examination of what it means in practice for a machine learning system to explain itself, or how explanation might or might not vindicate the normative goals that its champions support. While machine learning might be made to comply with existing laws, doing so may not furnish the actionable insights or persuasive justifications imagined by scholars, regulators, and advocates.