2020 Workshop on
Human Interpretability
in Machine Learning (WHI)

July 17, 2020

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.

Zoom link and live stream


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
12:30 PM - 2:00 PM
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
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)


Adrian Weller
University of Cambridge, The Alan Turing Institute / @adrian_weller
Alice Xiang
Partnership on AI / @alicexiang
Amit Dhurandhar
IBM AI Research
Been Kim
Google Brain / @_beenkim
Dennis Wei
IBM AI Research
Kush Varshney
IBM AI Research / @krvarshney
Umang Bhatt
University of Cambridge / @umangsbhatt
Hendrik Strobelt
Virtual Experience Chair
MIT-IBM Watson AI Lab / @hen_str