Keynote speakers

From Model Behaviour to Human Understanding: The Case for Actionable Explainable AI 

As deep learning systems grow more powerful yet more opaque, explainability becomes essential not just for trust, but for accountability, scientific discovery, and regulatory compliance. This keynote traces the evolution of XAI across levels of abstraction, from low-level attributions to human-readable rationales and counterfactuals, and confronts hard truths about evaluation, bias, and the limits of current methods. The talk closes with a call to make interpretability genuinely actionable, guiding model steering and supporting trustworthy AI deployment in high-stakes domains. 

Prof. Dr. Vera Schmitt is a professor at Johannes Gutenberg University Mainz and TU Berlin and founding head of the XplaiNLP research group, working at the intersection of NLP, Explainable AI, and Human-AI Interaction. Her research focuses on trustworthy, actionable AI for high-stakes domains such as fact-checking and clinical decision-making.

Explainable AI for Contestability

The need for explainability in AI is widely agreed upon as crucial towards safe and trustworthy deployment of AI systems, especially given the very many opportunities for undesired behaviour, including misinformation, hallucination and bias. In this talk I will advocate in particular  (X)AI approaches based on computational argumentation that can (1) interact to progressively explain outputs and/or reasoning as well as assess grounds for contestation provided by humans and/or other machines, and (2) revise decision-making processes to redress any issues successfully raised during contestation. I will ground the talk in LLM-based claim verification. 

Francesca Toni works at Imperial College London as Professor in Computational Logic in the Department of Computing, and leads the Computational Logic and Argumentation Group and the XAI@Imperial Centre. She holds an ERC Advanced grant on Argumentation-based Deep Interactive eXplanations (ADIX). Her research interests include explainable artificial intelligence, computational argumentation, and applications in healthcare, finance, law and chemistry.