International Workshop: Trust in the Age of AI

As AI systems increasingly mediate socially significant decisions, calls for trustworthy AI have become widespread. Yet trustworthiness requires clear mechanisms for determining whether an AI system actually satisfies ethical principles such as fairness, transparency, and accountability. Moreover, the complexity of contemporary AI models challenges our ability to systematically verify compliance with such normative requirements. This talk presents a logical approach to this problem, in which trustworthiness is treated as a post-hoc formally specifiable property of AI systems. By selecting ethically relevant predicates describing system behaviour and identifying admissible forms of their violation, it becomes possible to reason formally about whether a system satisfies trustworthiness conditions. The approach is illustrated through the case of algorithmic fairness, showing how formal specifications of fairness can guide the analysis of machine learning models and we discuss how these ideas inform the development of practical tools within the MIRAI (Milano Responsible AI) platform, bridging philosophical analysis, formal methods, and empirical auditing of AI systems.

Our reliance on AI systems as sources of information is steadily increasing. LLMs answer questions, explain complex topics, offer advice, and solve problems across a wide range of domains. As a result, users sometimes trust their outputs and treat them as experts or epistemic authorities. But is such trust justified? Do AI systems genuinely deserve the status of experts? While reliability is an important condition for expertise, the literature on expertise suggests that it is not sufficient. Experts are not merely reliable performers; they possess understanding of their domain. This raises a crucial question: can AI systems genuinely be said to understand? In this paper, we address this question by distinguishing between two forms of understanding. We argue that it is one thing to understand an epistemic mediator—such as a theory, model, or representational system—and another to understand phenomena or reality through the lens of such mediators. We call the former symbolic understanding and the latter noetic understanding. While symbolic understanding primarily consists in the ability to competently deploy epistemic mediators for explanation, inference, and prediction, noetic understanding requires a further element: an appropriate noetic profile, including rational commitment to the mediator as a plausible representation of reality. We argue that contemporary AI systems may exhibit some degree of symbolic understanding but lack noetic understanding. This distinction has important implications for the status of AI expertise: whether AI systems qualify as experts depends on whether expertise requires noetic understanding or whether symbolic understanding suffices.

Do labels such as “AI-generated” influence how people interpret online content? As deepfake videos and other AI-generated media spread rapidly on social media, platforms and regulators increasingly rely on transparency labels to inform users about manipulated or synthetic content. Yet such signals may do more than merely provide information: they may shape how viewers interpret the claims presented in the content itself. This is especially critical in the context of health claims, where misleading information can affect beliefs, judgments, and potentially behavior. Combining an eye-tracking experiment with an online experiment using AI-generated TikTok-style videos featuring factual and myth-based health claims, we investigate how AI-generated labels and fact-check labels guide visual attention, influence credibility judgments, and shape viewers’ interpretation of the same message. We further explore spillover effects by examining whether labeling some videos changes how users evaluate unlabeled content. Our findings aim to advance research on digital transparency, misinformation, and AI-mediated information environments by showing how labeling mechanisms shape user cognition and trust in health-related deepfake content. For practice, the study provides guidance on how to design transparency signals that inform users without unintentionally creating false reassurance or blanket skepticism.