collaborators

18 papers

cs.AI2026

Towards an Argumentative Foundation for Evaluative AI

Xiang Yin, Tim Miller, Nico Potyka +2

Evaluative AI (EAI) has been recently proposed as a way to support human decision-making, not by producing a single recommendation, but by presenting competing hypotheses together…

cs.CL2026

Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation

Eoghan Cunningham, Derek Greene, James Cross +1

Understanding how policy is debated and justified in parliament is a fundamental aspect of the democratic process. However, the volume and complexity of such debates mean that outs…

cs.AI2026

From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)

Aniol Civit, Antonio Rago, Antonio Andriella +2

Gradual argumentation is a field of symbolic AI which is attracting attention for its ability to support transparent and contestable AI systems. It is considered a useful tool in d…

cs.AI2026

Argumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us

Stylianos Loukas Vasileiou, Antonio Rago, Francesca Toni +1

Computational argumentation offers formal frameworks for transparent, verifiable reasoning but has traditionally been limited by its reliance on domain-specific information and ext…

cs.LG2026

Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders

Junqi Jiang, Francesco Leofante, Antonio Rago +1

Counterfactual explanations (CEs) provide recourse recommendations for individuals affected by algorithmic decisions. A key challenge is generating CEs that are robust against vari…

cs.AI2026

Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)

Deniz Gorur, Antonio Rago, Francesca Toni

Judgmental forecasting is the task of making predictions about future events based on human judgment. This task can be seen as a form of claim verification, where the claim corresp…