Workshop

5th ICML Workshop on Human Interpretability in Machine Learning (WHI)

Adrian Weller, Alice Xiang, Amit Dhurandhar, Been Kim, Dennis Wei, Kush Varshney, Umang Bhatt

Keywords:  causality    interpretability    explainability  

Abstract:

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.

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Schedule

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.
Break
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)