The Fifth Annual Workshop on Human Interpretability in Machine Learning (WHI 2020), held in conjunction with ICML 2020, will bring together artificial intelligence (AI) researchers who study the interpretability of AI systems, develop interpretable machine learning algorithms, and develop methodologies 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.
Schedule
all times in CEST/GMT+2
| Time | Event |
|---|---|
| 10:30 AM - 10:45 AM |
Contributed Talk: High Dimensional Model Explanations: an Axiomatic Approach
|
| 10:45 AM - 11:00 AM |
Contributed Talk: Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
|
| 11:00 AM - 11:30 AM |
Invited Talk: Fair, explainable and accountable AI in Europe: When Law meets Computer Science
:
Sandra Wachter
|
| 11:30 AM - 12:30 PM |
Spotlights
|
| 12:30 PM - 2:00 PM |
Break
|
| 2:00 PM - 2:30 PM |
Invited Talk: Intuitive and Interpretable Representation Learning
:
Finale Doshi-Velez
|
| 2:30 PM - 2:45 PM |
Contributed Talk: On the Privacy Risks of Model Explanations
|
| 2:45 PM - 3:00 PM |
Contributed Talk: The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons
|
| 3:00 PM - 3:30 PM |
Invited Talk
:
Donald Rubin
|
| 3:30 PM - 4:30 PM |
Spotlights
|
| 4:30 PM - 5:00 PM |
Invited Talk: Interpretability and Accountability Under the Law
:
Mason Kortz
|
| 5:00 PM - 5:45 PM |
Interpretability Panel- Taha Bahadori (Amazon), Finale Doshi-Velez (Harvard), Pang Wei Koh (Stanford), Lizzie Kumar (Utah), and Alice Xiang (Partnership on AI)
|
Organizers
Amit Dhurandhar
Co-Organizer
IBM AI Research
Dennis Wei
Co-Organizer
IBM AI Research
