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  


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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Timezone: America/Los_Angeles »