Lecture: Making explainable artificial intelligence understandable Szymon Bobek, Jagellonian University

What the talk will cover:

• Lessons from industrial use-cases: predictive maintenance,explainable anomaly detection, and visual quality inspection.
• Evidence from  a user-centric evaluation of explanation methods.
• Explainability methods based on rules, prototypes, counterfactuals, and feature attribution.
• Open research directions in explainability with neurosymbolic AI, LLMs, VLMs, eye-tracking analysis and conversational systems for interactive, multimodal and expert-aligned explanations.