TransparencyTrustworthinessSafetycs.HC
The Transparency Trap: How AI Disclaimers Create Overconfidence in High-Stakes Decisions
Neil Todkar
June 16, 2026
Key Takeaways
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Disclaimers can backfire: The study found a "transparency paradox" where some people interpreted AI disclaimers not as warnings but as evidence that the system is honest and self-aware, actually increasing trust rather than reducing it—the opposite of what was intended.
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People ignore standardized warnings: The research identified "banner blindness," meaning users routinely overlook standard AI disclaimers, suggesting that current disclaimer formats are insufficient to prevent people from over-relying on AI systems in important decisions about finances, health, and other high-stakes matters.
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Medical AI is trusted most: Across the three domains tested (finance, medicine, and AI-generated content), medical information received the highest trust ratings from participants, even when disclaimers were present—raising concerns about potential harms in healthcare contexts.
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Context shapes how people interpret warnings: The study showed that users interpret disclaimers differently depending on the domain and their perception of risk, meaning a one-size-fits-all disclaimer approach won't work effectively across different types of decisions.
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Current AI design practices may enable overconfidence: The findings suggest that simply adding disclaimers to AI systems is not an adequate safeguard for responsible AI; designers need fundamentally different approaches to help people make better decisions when relying on AI in high-stakes situations.
Abstract
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 significant domain effect showed that medical content received the highest trust ratings. In the AI domain, the findings reveal a transparency paradox: some participants interpreted disclaimers not as warnings, but as signs of system self-awareness and honesty, paradoxically increasing perceived trustworthiness. Evidence of banner blindness further suggests that standardized AI disclaimers are insufficient to prevent over-reliance. Finance and medicine provide useful comparison domains by showing how users interpret warnings differently depending on context and perceived risk. These findings have vital implications for responsible AI design, algorithmic fairness, and consumer protection when users act on potentially misleading information in high-stakes settings.
6 pages, 4 figures
Paper Info
Responsible AI Topics
TransparencyTrustworthinessSafety
Links
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