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 Workshop Website Join Zoom
Please do not share or post zoom links
Fri Jul 17, 1 a.m. PDT


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.

Chat is not available.

Timezone: »


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)