Workshops

Summer 2022

Interpretable Machine Learning in Natural and Social Sciences

Jun 28, 2022 to Jun 30, 2022

Return to event »

All talks are in PST Time. Schedule subject to change.

Tuesday, June 28th, 2022
9:00 am – 9:20 am		Coffee and Check-In
9:20 am – 9:30 am		Opening Remarks
9:30 am – 10:00 am		Goals And Interpretable Variables In Neuroscience David Danks (University of California, San Diego)
10:00 am – 10:30 am		Break
10:30 am – 11:00 am		Accuracy and Interpretability Through the Lens of Human-AI Teaming Sina Fazelpour (Northeastern University)
11:00 am – 12:00 pm		Panel on Societal Dimensions of Explanation David Danks (University of California, San Diego), and Sina Fazelpour (Northeastern University)
12:00 pm – 3:00 pm		Lunch
3:00 pm – 3:30 pm		Conceptual Challenges In Connecting Interpretability And Causality Alex D'Amour (Google Brain)
3:30 pm – 4:00 pm		Break
4:00 pm – 4:30 pm		Explanation: A(N Abridged) Survey Joe Halpern (Cornell University)
4:30 pm – 5:30 pm		Panel on Causality Alex D'Amour (Google Brain), and Joe Halpern (Cornell University)
5:30 pm – 6:30 pm		Reception

Wednesday, June 29th, 2022
9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:00 am		Interpretable Machine Learning For Astrophysics Shirley Ho (Flatiron Institute/ NYU) [REMOTE]
10:00 am – 10:30 am		Interpretability In Atomic-Scale Machine Learning Michele Ceriotti (Swiss Federal Institute of Technology in Lausanne) [REMOTE]
10:30 am – 11:00 am		Break
11:00 am – 11:30 am		Interpreting Machine Learning From the Perspective of Nonequilibrium Systems David Limmer (University of California, Berkeley)
11:30 am – 12:30 pm		Panel on Interpretability in the Physical Sciences Shirley Ho (Flatiron Institute/ NYU) [REMOTE], Michele Ceriotti (Swiss Federal Institute of Technology in Lausanne) [REMOTE], and David Limmer (University of California, Berkeley)
12:30 pm – 2:30 pm		Lunch
2:30 pm – 3:00 pm		Generative Deep Models of Biological Sequences for Function and Design Debbie Marks (Harvard University)
3:00 pm – 3:30 pm		Interpreting Deep Learning Models Of Functional Genomics Data To Decode Regulatory Sequence Syntax And Genetic Variation Anshul Kundaje (Stanford University) [REMOTE]
3:30 pm – 4:00 pm		Break
4:00 pm – 4:30 pm		Machine Learning, Deep Networks and Interpretability in Systems, Cognitive and Computational Neuroscience Jack Gallant (University of California, Berkeley)
4:30 pm – 5:30 pm		Panel on Interpretability in the Biological Sciences Debbie Marks (Harvard University), Anshul Kundaje (Stanford University) [REMOTE], and Jack Gallant (University of California, Berkeley)

Thursday, June 30th, 2022
9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:00 am		Interpretability and Algorithmic Fairness Alice Xiang (Sony AI) [REMOTE]
10:00 am – 10:30 am		Break
10:30 am – 11:00 am		Legal Barriers To Interpretable Machine Learning Rebecca Wexler (Berkeley Center for Law & Technology) [REMOTE]
11:00 am – 11:30 am		Platform-supported Auditing Of Social Media Algorithms For Public Interest Aleksandra Korolova (University of Southern California) [REMOTE]
11:30 am – 12:30 pm		Panel on Interpretability in the Law Alice Xiang (Sony AI), Aleksandra Korolova (University of Southern California), and Rebecca Wexler (Berkeley Center for Law & Technology) [REMOTE]
12:30 pm – 2:00 pm		Group Lunch (boxed lunches provided)
2:00 pm – 4:30 pm		Group hike