The use of machine learning in business, government, and other settings that require users to understand the model’s predictions has exploded in recent years. This growth, combined with the increased popularity of opaque ML models like deep learning, has led to the development of a thriving field of model explainability research and practice.

In this panel discussion, we bring together experts and researchers to explore the current state of explainability and some of the key emerging ideas shaping the field. Each guest will share their unique perspective and contributions to thinking about model explainability in a practical way.

Join us as we explore concepts like stakeholder-driven explainability, adversarial attacks on explainability methods, counterfactual explanations, legal and policy implications, and more. We round out the session with an audience Q&A! Check out the list of resources below!


Rayid GhaniCarnegie Mellon University

Solon Barokas – Cornell, Microsoft

Kush R. VarshneyIBM

Alessya Labzhinova – Stealth

Hima LakkarajuHarvard 

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Rayid Ghani, Carnegie Mellon University – Professor in the Machine Learning Department (in the School of Computer Science) and the Heinz College of Information Systems and Public Policy

Solon Barocas, Cornell University – Assistant Professor, Department of Information Science, Principal Researcher at Microsoft Research

Kush R. Varshney, IBM, Distinguished Research Staff Member and Manager at IBM Thomas J. Watson Research Center

Alessya Labzhinova, CEO of a stealth startup and former CTO in residence AI2

Hima Lakkaraju, Harvard  University Assistant Professor with appointments in Business School and Department of Computer Science