Towards an Argumentative Foundation for Evaluative AI

Computer Science > Artificial Intelligence [Submitted on 25 Apr 2026] Towards an Argumentative Foundation for Evaluative AI Xiang Yin, Tim Miller, Nico Potyka, Antonio Rago, Francesca Toni 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 with evidence for and against each. In this position paper, we advocate (computational) argumentation as a particularly suitable paradigm to provide a formal, computable foundation for forms of EAI that are explainable and contestable, setting the ground for a long-term research agenda towards distributed and human-centred EAI systems. Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2608.07473 [cs.AI]   (or arXiv:2608.07473v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2608.07473 Focus to learn more Submission history From: Xiang Yin [view email] [v1] Sat, 25 Apr 2026 13:26:11 UTC (131 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)