This position paper proposes computational argumentation as the formal basis for Evaluative AI (EAI), a paradigm that supports human decision-making by presenting competing hypotheses with supporting and opposing evidence. The authors argue this approach ensures systems are both explainable and contestable, moving beyond single-recommendation models. The work outlines a long-term research agenda focused on developing distributed, human-centered EAI systems.
- EAI shifts focus from single recommendations to presenting competing hypotheses with evidence.
- Computational argumentation provides a formal, computable foundation for explainable AI.
- Systems are designed to be contestable, allowing users to challenge underlying logic.
- Research agenda targets distributed and human-centered AI architectures.