XAI – Explanation in AI: From Machine Learning to Knowledge Representation & Reasoning and Beyond


The term XAI refers to a set of tools for explaining AI systems of any kind, beyond Machine Learning. Even though these tools aim at addressing explanation in the broader sense, they are not designed for all users, tasks, contexts and applications. This presentation will describe progress to date on XAI by reviewing its approaches, motivation, best practices, industrial applications, and limitations.

Session Outline
Part I: Introduction, Motivation & Evaluation
Part II: Explanation in AI (not only Machine Learning!) (focus ML)
Part IV: XAI Applications and Lessons Learnt
Part V: XAI Tools, Coding Practices Conclusion, and Research Challenges

Background Knowledge
Machine Learning principles, Python


Dr. Freddy Lecue is the Chief Artificial Intelligence (AI) Scientist at CortAIx (Centre of Research & Technology in Artificial Intelligence eXpertise) at Thales in Montreal - Canada. He is also a research associate at INRIA, in WIMMICS, Sophia Antipolis - France. Before joining the new R&T lab of Thales dedicated to AI, he was AI R&D Lead at Accenture Labs in Ireland from 2016 to 2018. Prior to joining Accenture he was a research scientist, the lead investigator in large-scale reasoning systems at IBM Research from 2011 to 2016, a research fellow at The University of Manchester from 2008 to 2011, and a research engineer at Orange Labs from 2005 to 2008.

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