AI³ 2026

Prof. Henry Prakken, University of Utrecht

On Relevance and Explanations in Abstract Argumentation Frameworks

Joint work with Giuseppe Pisano and Giovanni Sartor (Unibo, Bologna).

In this talk I will discuss new set-based definitions of relevance and explanation for abstract argumentation frameworks, aiming to overcome some limitations of existing approaches. I will discuss relations between definitions of explanations of set-membership in terms of (strongly) admissible sets and with notions of winning strategies in argument games for preferred, grounded and ideal semantics. An important insight is that it is challenging to give a fully unified treatment for different semantics.

Prof. Annalisa Coliva, University of California, Irvine

Can We Trust AI? A Philosophical Framework for Responsible Trust in Artificial Intelligence

Joint work with Alessio Tacca (IUSS, Pavia).

Discussions of AI trustworthiness are widespread yet marked by persistent conceptual confusion. This presentation, drawing on Coliva (2025, 2026) and Coliva & Tacca (ms.), proposes a rigorous philosophical framework by distinguishing three questions that the literature routinely conflates: whether trust in AI is conceptually legitimate, whether AI systems are trustworthy, and whether a user's trust in AI is responsible. On the first question, we argue that trust in AI is philosophically well-motivated once trust is understood — following Nguyen (2022) and Coliva (2025) — as a default stance of unquestioning reliance rather than a morally loaded interpersonal relation. On the second, we establish that AI trustworthiness is always task-relative, context-dependent, and defeasible, identifying eight structural categories of epistemic defeater including hallucination, systematic bias, distributional shift, adversarial vulnerability, and miscalibration. On the third question, we argue that responsible trust requires genuine defeater sensitivity at the task, domain, and system levels — going beyond mere outcome alignment. We further identify deskilling and automation bias as the two primary systemic threats to responsible trust, and conclude with implications for AI governance, institutional design, and education in epistemic vigilance.