Introduction to Explainable AI: Techniques & Design

As Artificial Intelligence (AI) technologies are increasingly used to make important decisions and perform autonomous tasks, the need to provide explanations to allow users and stakeholders to understand the AI has become a ubiquitous concern. Recently, a number of open-source toolkits are making the growing collection of Explainable AI (XAI) techniques accessible for researchers and practitioners to incorporate explanation features in their AI systems. This talk aims to provide an overview on the technical and design methods for XAI for those interested in implementing or designing explainable AI systems.

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About Vera Liao

Vera Liao is a Principal Researcher at Microsoft Research Montréal where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics of AI) group. Her current research interests are in human-AI interaction, explainable AI, and responsible AI. Prior to joining MSR, she worked at IBM T.J. Watson Research Center. She currently serves as the Co-Editor-in-Chief for Springer HCI Book Series, in the Editors team for ACM CSCW conferences, and on the Editorial Board of ACM Transactions on Interactive Intelligent Systems (TiiS).