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5th ICML Workshop on Human Interpretability in Machine Learning (WHI)
Adrian Weller · Alice Xiang · Amit Dhurandhar · Been Kim · Dennis Wei · Kush Varshney · Umang Bhatt

Fri Jul 17 01:00 AM -- 05:00 PM (PDT) @
Event URL: https://sites.google.com/view/whi2020 »

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.

Author Information

Adrian Weller (University of Cambridge, Alan Turing Institute)
Adrian Weller

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, and is a Turing AI Fellow leading work on trustworthy Machine Learning (ML). He is a Principal Research Fellow in ML at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.

Alice Xiang (Partnership on AI)
Amit Dhurandhar (IBM Research)
Been Kim (Google)
Dennis Wei (IBM Research)
Kush Varshney (IBM Research AI)
Umang Bhatt (University of Cambridge)

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