BIDS Faculty Affiliate Bin Yu is an organizer of this Simons Institute workshop on Interpretable Machine Learning in Natural and Social Sciences, which will convene an interdisciplinary group of scholars – including BIDS Faculty Affiliate Rebecca Wexler – 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.
Organizers: Himabindu Lakkaraju (Harvard University), Zachary Lipton (Carnegie Mellon University), David Madigan (Columbia University), Deirdre Mulligan (UC Berkeley), Bin Yu (UC Berkeley)
Bin Yu is Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the Departments of Statistics and of Electrical Engineering & Computer Science, and Center for Computational Biology at the University of California, Berkeley. She is an Investigator with the Weill Neurohub, a collaboration of the University of California, Berkeley (UC Berkeley), the University of California, San Francisco (UCSF), and the University of Washington (the UW). She leads the Yu Group at Berkeley, which is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine. In order to solve data problems in these domain areas, her group employs quantitative critical thinking and develops statistical and machine learning algorithms and theory. She has published more than 100 scientific papers in premier journals in statistics, machine learning, information theory, signal processing, remote sensing, neuroscience, genomics, and networks. She was a Guggenheim Fellow and President of Institute of Mathematical Statistics (IMS), and is a member of the U.S. National Academy of Sciences and fellow of the American Academy of Arts and Sciences.
Rebecca Wexler is an Assistant Professor of Law at the University of California, Berkeley, School of Law, where she teaches, researches, and writes on issues concerning data, technology, and criminal justice. Her work has focused on evidence law, criminal procedure, privacy, and intellectual property protections surrounding new data-driven criminal justice technologies. She is also a Faculty Co-Director of the Berkeley Center for Law & Technology.
Professor Wexler’s research includes Life Liberty and Trade Secrets: Intellectual Property in the Criminal Justice System, The Stanford Law Review (2018); Technology’s Continuum: Body Cameras, Data Collection, and Constitutional Searches, in Visual Imagery and Human Rights Practice (2018); Gags as Guidance: Expanding Notice of National Security Letter Investigations to Targets and the Public, The Berkeley Technology Law Journal (2016); The Private Life of DRM: How Fundamental Rights Frame Copyright Enforcement Reform, The Yale Journal of Law & Technology (2015); and Warrant Canaries and Disclosure by Design: The Real Threat to National Security Letter Gag Orders, The Yale Law Journal Forum (2014). Her work has appeared in The New York Times, The LA Times, Washington Monthly, Slate, and has been featured on NPR.