Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
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Artificial Intelligence technologies are increasingly used to make decisions and perform autonomous tasks in critical domains. The need to understand AI in order to improve, contest, develop appropriate trust, and better interact with AI systems has spurred great academic and public interest in Explainable AI (XAI). The technical field of XAI has produced a vast collection of algorithms in recent years. However, explainability is an inherently human-centric property and the field is starting to embrace human-centered approaches. Human-computer interaction (HCI) research and user experience (UX) design in this area are increasingly important especially as practitioners begin to leverage XAI algorithms to build XAI applications. In this talk, I will draw on our research and broad HCI works to highlight the central role that human-centered approaches should play in shaping XAI technologies, including driving technical choices by understanding users’ explainability needs, uncovering pitfalls of existing XAI methods, and providing conceptual frameworks for human-compatible XAI.
Q. 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, and studied at the University of Illinois at Urbana-Champaign and Tsinghua University. Her research received multiple paper awards at ACM CHI and IUI. 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). She actively organizes events that connect the HCI and AI communities, including several workshops and panels at CHI, IUI, and CSCW conferences.