Weiwei Yi

Weiwei Yi, Zihao Li

Dark patterns, design tricks used on online interfaces to manipulate users decision-making process, have raised public concerns. However, research on regulation of dark pattern remains underdeveloped and scattered, particularly regarding scholars views on the concept, regulatory paradigms, and solutions. Following PRISMA guidelines, this paper systematically reviews the formats and content of regulatory discussions on dark patterns from the interdisciplinary scholarship of Law and Human-Computer Interaction. A total of 65 studies were analysed through content and thematic analysis. This study synthesises the unique trends and characteristics of legal scholarship on dark patterns, identifying five root problems and triple layered harms. It critiques current regulations in terms of legal theories and sectoral legislations, highlighting their inadequacies in addressing dark patterns. The paper also critically examines existing proposed solutions, including paradigmatic shifts in legal doctrines, refinements to existing frameworks, technical design-embedded solutions, and accountability measures for design practices. This research critically discusses the current barriers to effective dark pattern regulations and explores promising regulatory solutions. The difficulty in identifying the normative nature of various forms of dark patterns, in identifying evident and actionable harm, and the expanding scope of dark patterns connotation inherently hinders effective regulation. However, technical design-embedded solutions, accountability frameworks, and practical design guidelines offer potential routes for more proactive regulation, while legal pluralism stands as a promising macro-level change in regulatory paradigms for dark pattern regulation.

Zihao Li, Weiwei Yi, Jiahong Chen

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.