This tutorial delves into the domain of computational argumentation within AI, emphasising its crucial role in managing incomplete or inconsistent information. In the context of AI, computational argumentation leverages logical foundations and rule-governed mechanisms to enable reasoning engines, while its dialectical nature and affinity with common-sense reasoning make it more comprehensible to users.

Designed as a half-day session, the tutorial will be highly interactive, blending traditional lectures with engaging elements like games and demonstrations. Attendees will gain practical insights into how computational argumentation enhances the capabilities of AI systems. By combining theoretical foundations with interactive activities, the tutorial seeks to equip participants with a nuanced understanding of computational argumentation's practical implications and its potential to reshape the landscape of AI reasoning and decision-making. AI. Our tutorial is targeted at students, researchers, and practitioners with an interest in human-centered AI, with no prior knowledge of computational argumentation required. The only requirement is bringing a laptop (or share one between a pair).

Presenters

Elfia Bezou-Vrakatseli

Elfia Bezou-Vrakatseli (elfia.bezou_vrakatseli@kcl.ac.uk) is a PhD candidate in the UK Research and Innovation Centre for Doctoral Training in Safe and Trusted Artificial Intelligence at King's College London, where she is also the representative of the Equality, Diversity & Inclusion committee. Her expertise is in argumentation and her research focuses on using argumentation tools to enhance the communication between humans and between humans and AI systems.

Madeleine Waller

Madeleine Waller (madeleine.waller@kcl.ac.uk) is a PhD candidate in the UK Research and Innovation Centre for Doctoral Training in Safe and Trusted Artificial Intelligence at King's College London. She is an Affiliate of the King's Institute for Artificial Intelligence and currently taking part in The Alan Turing Institute's Enrichment Scheme. Her research and expertise lie in the connection between fairness and explainablity of AI systems, specifically using argumentation-based approaches.