The Transparency Trap: How AI Disclaimers Create Overconfidence in High-Stakes Decisions
Current AI disclaimers often fail to function as intended due to warning habituation and a transparency paradox. As AI-generated information becomes pervasive in everyday decision-making, effective risk communication is increasingly critical for responsible design. This exploratory study examines how disclaimer placement and persuasive cues shape trust, perceived accuracy, and disclaimer engagement across three high-stakes domains: finance, medicine, and AI-generated content. Using a mixed within-between experimental design with 378 stimulus-level responses from 52 participants, we find that advisory content was generally trusted across conditions, even when disclaimers were present. A si...
| Authors | Neil Todkar |
|---|---|
| Primary category | cs.HC |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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