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5th ICML Workshop on Human Interpretability in Machine Learning (WHI)
Adrian Weller · Alice Xiang · Amit Dhurandhar · Been Kim · Dennis Wei · Kush Varshney · Umang Bhatt

Fri Jul 17 01:00 AM -- 05:00 PM (PDT) @ None
Event URL: https://sites.google.com/view/whi2020 »

This workshop will bring together artificial intelligence (AI) researchers who study the interpretability of AI systems, develop interpretable machine learning algorithms, and develop methodology to interpret black-box machine learning models (e.g., post-hoc interpretations). This is a very exciting time to study interpretable machine learning, as the advances in large-scale optimization and Bayesian inference that have enabled the rise of black-box machine learning are now also starting to be exploited to develop principled approaches to large-scale interpretable machine learning. Interpretability also forms a key bridge between machine learning and other AI research directions such as machine reasoning and planning. Participants in the workshop will exchange ideas on these and allied topics.

Fri 1:30 a.m. - 2:00 a.m.
Contributed Talk 1 and 2 (Talk)
Fri 2:00 a.m. - 2:30 a.m.
Invited Talk: Sandra Wachter (Keynote)
Fri 2:30 a.m. - 3:30 a.m.
Spotlights (Talk)
Fri 3:30 a.m. - 5:00 a.m.
Fri 5:00 a.m. - 5:30 a.m.
Invited Talk: Finale Doshi-Velez (Keynote)
Fri 5:30 a.m. - 6:00 a.m.
Contributed Talk 3 and 4 (Talk)
Fri 6:00 a.m. - 6:30 a.m.
Invited Talk: Donald Rubin (Keynote)
Fri 6:30 a.m. - 7:30 a.m.
Spotlights (Talk)
Fri 7:30 a.m. - 8:00 a.m.
Invited Talk: Mason Kortz (Keynote)
Fri 8:00 a.m. - 8:45 a.m.
Interpretability Panel (Panel)

Author Information

Adrian Weller (University of Cambridge, Alan Turing Institute)

Adrian Weller is a Senior Research Fellow in the Machine Learning Group at the University of Cambridge, a Faculty Fellow at the Alan Turing Institute for data science and an Executive Fellow at the Leverhulme Centre for the Future of Intelligence (CFI). He is very interested in all aspects of artificial intelligence, its commercial applications and how it may be used to benefit society. At the CFI, he leads their project on Trust and Transparency. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.

Alice Xiang (Partnership on AI)
Amit Dhurandhar (IBM Research)
Been Kim (Google)
Dennis Wei (IBM Research)
Kush Varshney (IBM Research AI)
Umang Bhatt (University of Cambridge)

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