Towards an Argumentative Foundation for Evaluative AI
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.
| Authors | Xiang Yin, Tim Miller, Nico Potyka et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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