HDSI Seminar Series: Unavoidable Tensions in Explaining Algorithmic Decisions

Title: Unavoidable Tensions in Explaining Algorithmic Decisions

Abstract: Recent developments in methods for explaining the decisions of machine learning models have been widely embraced for their ability to provide transparency and accountability without limiting model complexity or compelling model disclosure. Yet applying these methods is far from straightforward and they rarely prove a cure all. This talk identifies a number of unavoidable tensions that decision makers must navigate as they seek to employ these methods—and the deeply subjective judgment that must go into these considerations.

Papers: This presentation is based–and builds–on two papers, which are joint work with Andrew Selbst and Manish Raghavan:

• Barocas, Solon, Andrew D. Selbst, and Manish Raghavan. “The hidden assumptions behind counterfactual explanations and principal reasons.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 80-89. 2020. https://dl.acm.org/doi/abs/10.1145/3351095.3372830
• Selbst, Andrew D., and Solon Barocas. “The Intuitive Appeal of Explainable Machines.” Fordham Law Review 87, no. 3 (2018): 1085. https://ir.lawnet.fordham.edu/cgi/viewcontent.cgi?article=5569&context=flr

Bio:  Solon Barocas is a Principal Researcher in the New York City lab of Microsoft Research, an Adjunct Assistant Professor in the Department of Information Science at Cornell, and Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard. 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. Solon co-founded the ACM conference on Fairness, Accountability, and Transparency (FAccT).