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Title: The Algorithmic Explainability Bait and Switch
Speaker: Assistant Professor, Department of Statistical Sciences & Department of Philosophy, University of Toronto
Date: June 16, 2023
Time: 11:15am – 12:30pm

Mode: Hybrid. Seats for on-site participants are limited. A confirmation email will be sent to participants who have successfully registered.

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Abstract

Explainability in machine learning (ML) is emerging as a leading area of academic research and a topic of significant legal and regulatory concern. Indeed, a near-consensus is emerging in favour of explainable ML among lawmakers, academics, and civil society groups. In this project, we challenge this prevailing trend. We argue that explaining ML predictions is at best unnecessary or misleading and at worst socially harmful. Unlike interpretable ML, which we endorse where it is feasible, explainable ML can deliver on none of the benefits it is touted for – e.g., engendering trust, increasing understanding, and promoting algorithmic safety and reliability.

Speaker

Dr. Boris Babic
Assistant Professor @ Department of Statistical Sciences & Department of Philosophy, University of Toronto
Boris Babic is a tenure track assistant professor at The University of Toronto, where he has a joint appointment in the Department of Statistical Sciences and the Department of Philosophy. He is also a faculty fellow of the Schwartz Reisman Institute for Technology and Society, and a visiting assistant professor of Decision Sciences at INSEAD. He received a PhD in Philosophy and an MSc in Statistics from the University of Michigan, Ann Arbor, and a JD from Harvard Law School. He completed his postdoctoral scholarship at the California Institute of Technology (Caltech). His primary research interests are in legal, ethical, and policy dimensions of artificial intelligence and machine learning as well as in the foundations of Bayesian inference. He is one of the founding associate editors of ACM Transactions on Probabilistic Machine Learning. His work has appeared in Science, Nature Machine Intelligence, Nature Digital Medicine, Philosophy of Science, the British Journal for the Philosophy of Science, and the Harvard Business review, among others.

For information, please contact:
Email: datascience@hku.hk