Beyond Accuracy: Rethinking Hallucination and Regulatory Response in Generative AI
This work addresses the problem of inadequate regulatory responses to AI hallucination for policymakers and developers, but it is incremental as it builds on existing critiques without introducing new technical solutions.
The paper critiques the narrow technical view of hallucination in generative AI, arguing that current regulatory frameworks fail to address its broader risks like ambiguity and bias, and proposes a layered approach for governance that accounts for meaning and social impact.
Hallucination in generative AI is often treated as a technical failure to produce factually correct output. Yet this framing underrepresents the broader significance of hallucinated content in language models, which may appear fluent, persuasive, and contextually appropriate while conveying distortions that escape conventional accuracy checks. This paper critically examines how regulatory and evaluation frameworks have inherited a narrow view of hallucination, one that prioritises surface verifiability over deeper questions of meaning, influence, and impact. We propose a layered approach to understanding hallucination risks, encompassing epistemic instability, user misdirection, and social-scale effects. Drawing on interdisciplinary sources and examining instruments such as the EU AI Act and the GDPR, we show that current governance models struggle to address hallucination when it manifests as ambiguity, bias reinforcement, or normative convergence. Rather than improving factual precision alone, we argue for regulatory responses that account for languages generative nature, the asymmetries between system and user, and the shifting boundaries between information, persuasion, and harm.