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Training an AI to render accurate decisions for important questions can be useless and dangerous if it cannot tell you why it made those decisions. Enter explainability, a term so new that it isn't in spellcheckers but is critical to the successful future of AI in critical applications.

Before I talked with Michael Hind, my usual remark on the subject was, "If you want a demonstration of the ultimate futility of explainability, try asking your kid how the vase got broken." But after this episode I've learned more than I thought possible about how we can teach AI what an explanation is and how to produce one.

Michael is a Distinguished Research Staff Member in the IBM
Research AI department in Yorktown Heights, New York.  His current
research passion is the area of Trusted AI, focusing on governance,
transparency, explainability, and fairness of AI systems.  He helped launch several successful open source projects, such as
AI Fairness 360 and AI Explainability 360.

All this plus our usual look at today's AI headlines.

Transcript and URLs referenced at HumanCusp Blog.

Michael Hind





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