Speakers
Ruth Byrne
Talk title: How People Reason about Counterfactual Explanations for Decisions by Artificial Intelligence Systems
Ruth Byrne is the Professor of Cognitive Science at Trinity College Dublin, University of Dublin, in the School of Psychology and the Institute of Neuroscience. Her research expertise is in the cognitive science of human thinking, including experimental and computational investigations of reasoning and imaginative thought. Her books include "The Rational Imagination: How People Create Alternatives to Reality" (2005, MIT press). She is the former Vice Provost of Trinity College Dublin, a member of the Royal Irish Academy, and a senior editor for the journal Cognitive Science.
Alison Gopnik
Talk title: Counterfactuals, play and causal inference in young children and machines
Alison Gopnik is a professor of psychology and affiliate professor of philosophy at the University of California at Berkeley, and a member of the Berkeley AI Research Group. She received her BA from McGill University and her PhD. from Oxford University. She is a leader in the study of cognitive science and of children’s learning and development and was one of the founders of the field of “theory of mind”, an originator of the “theory theory” of cognitive development, and the first to apply Bayesian probabilistic models to children’s learning.
She has received both the APS Lifetime Achievement Cattell and William James Awards, the Bradford Washburn Award for Science Communication, and the SRCD Lifetime Achievement Award for Basic Science in Child Development. She is an elected member of the Society of Experimental Psychologists and the American Academy of Arts and Sciences and a Cognitive Science Society, American Association for the Advancement of Science, and Guggenheim Fellow. She was 2022-23 President of the Association for Psychological Science.
Thomas Icard
Talk title: What's so special about counterfactuals?
Thomas is Professor of Philosophy and (by courtesy) of Computer Science at Stanford University. He works at the intersection of philosophy, cognitive science, and computer science, especially on topics that sit near the boundary between the normative (how we ought to think and act) and the descriptive (how we in fact think and act). Much of his research concerns the theory and application of logic, probability, and causal modeling and inference. Some current topics of interest include explanation, semantics, the quantitative/qualitative interface, and decision making with limited resources.
Himabindu Lakkaraju
Talk title: Regulating Explainable AI: Technical Challenges and Opportunities
Himabindu (Hima) Lakkaraju is an assistant professor at Harvard University focusing on the algorithmic, theoretical, and applied aspects of explainability, fairness, and robustness of machine learning models. Hima has been named as one of the world’s top innovators under 35 by both MIT Tech Review and Vanity Fair. She also received several prestigious awards including the NSF CAREER award and multiple best paper awards at top-tier ML conferences, and grants from NSF, Google, Amazon, JP Morgan, and Bayer. Hima has given keynote talks at various top ML conferences and associated workshops including CIKM, ICML, NeurIPS, ICLR, AAAI, and CVPR, and her research has also been showcased by popular media outlets including the New York Times, MIT Tech Review, TIME magazine, and Forbes. More recently, she co-founded the Trustworthy ML Initiative to enable easy access to resources on trustworthy ML and to build a community of researchers/practitioners working on the topic.
Jonathan Richens
Talk title: Counterfactual reasoning is necessary for avoiding harm
Jon is a research scientist at DeepMind, researching the role of causality in AI safety and alignment. Previously, he has worked on applied ML for medicine, with a focus on causal methods for clinical decision making.
Suchi Saria
Talk title: TBD
Suchi Saria, PhD, holds the John C. Malone endowed chair and is the Director of the Machine Learning, AI and Healthcare Lab at Johns Hopkins. She is also is the Founder and CEO of Bayesian Health. Her research has pioneered the development of next generation diagnostic and treatment planning tools that use statistical machine learning methods to individualize care. She has written several of the seminal papers in the field of ML and its use for improving patient care and has given over 300 invited keynotes and talks to organizations including the NAM, NAS, and NIH. Dr. Saria has served as an advisor to multiple Fortune 500 companies and her work has been funded by leading organizations including the NIH, FDA, NSF, DARPA and CDC.
Mihaela van der Schaar
Talk title: Causal Deep Learning
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).
Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.