Summer 2022

Interpretable Machine Learning in Natural and Social Sciences

Jun 28, 2022 to Jun 30, 2022 

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Zachary Lipton (Carnegie Mellon University; co-chair), Bin Yu (UC Berkeley; co-chair), Himabindu Lakkaraju (Harvard University), David Madigan (Columbia University), Deirdre Mulligan (UC Berkeley)

This workshop will convene an interdisciplinary group of scholars to inspire clear foundational formulations of interpretability in a variety of domains where questions of interpretability arise in the application of machine learning, statistics, and data science more broadly. The attendees will include scholars from both the natural sciences — including precision medicine and the physical, biological and neuroscience sciences, and the social sciences — including political science, economics, and law, together with machine learners, statisticians, and data scientists. Across these domains, the term "interpretability" is often overloaded to speak to such disparate concerns as assisting in model checking, comparing extracted patterns against domain knowledge, extracting insights and generating hypotheses, anticipating failures on out-of-domain data, and providing accountability and contestability to individuals subject to data-driven decision-making. 

Our goal is to collectively characterize both the problems where interpretability concerns arise and the concepts that have arisen in recent efforts to address them. We aim to develop the appropriate theoretical framing of interpretability in the context of these domain problems. In particular, we will discuss and clarify the role of causal inference in various interpretability problems. We will identify desirable theoretical results under these concepts and framing, and discuss steps to test or validate the applicability of these results to the domain problems that motivate us. Where possible, we will propose performance metrics and discuss a workflow for continuously revising our framing to be useful in the intended domain areas for guiding both decision-making and knowledge generation.

Everyone is welcome to attend this workshop. Registration is required. The link to the registration form will appear at the top of this page approximately 10 weeks before the workshop. Space may be limited, and you are advised to register early. To submit your name for consideration, please register and await confirmation of your acceptance before booking your travel.

Further details about this workshop will be posted in due course. To contact the organizers about this workshop, please complete this form. (Please note the form is not for registration.)

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Speaker: David Danks (University of California, San Diego)
Speaker: David Danks (University of California, San Diego), and Sina Fazelpour (Northeastern University)
Speaker: Joe Halpern (Cornell University)
Speaker: Alex D'Amour (Google Brain), and Joe Halpern (Cornell University)
Speaker: Michele Ceriotti (Swiss Federal Institute of Technology in Lausanne) [REMOTE]
Speaker: Michele Ceriotti (Swiss Federal Institute of Technology in Lausanne) [REMOTE], and David Limmer (University of California, Berkeley)
Speaker: Anshul Kundaje (Stanford University) [REMOTE], and Jack Gallant (University of California, Berkeley)
Speaker: Rebecca Wexler (Berkeley Center for Law & Technology) [REMOTE]
Speaker: Alice Xiang (Sony AI), Aleksandra Korolova (University of Southern California), and Rebecca Wexler (Berkeley Center for Law & Technology) [REMOTE]
Invited Participants: 
Michele Ceriotti (EPFL), Alex D'Amour (Google), David Danks (UC San Diego), Sina Fazelpour (Northeastern University), Jack Gallant (UC Berkeley), Joseph Halpern (Cornell University), Tad Hirsch (Northeastern), Aleksandra Korolova (USC), Anshul Kundaje (Stanford University), David Limmer (UC Berkeley), David Limmer (UC Berkeley), Jas Sekhon (Yale University), Ruth Urner (York University), Rebecca Wexler (UC Berkeley)