Agenda
● Part I: Introduction and Motivation
○ Motivation, Definitions, Properties, Evaluation
○ Challenges for Explainable AI @ Scale
● Part II: Explanation in AI (not only Machine Learning!)
○ From Machine Learning to Knowledge Representation and Reasoning and Beyond
● Part III: Explainable Machine Learning (from a Machine Learning Perspective)
● Part IV: Explainable Machine Learning (from a Knowledge Graph Perspective)
● Part V: Case Studies from Industry
○ Applications, Lessons Learned, and Research Challenges
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