Conceptual analysis reveals how AI safety signals drive risk compensation in health consumers, highlighting algorithmic reassurance as an unintended driver of patient harm.
Key Points
To establish a theoretical framework explaining how safety cues marketed for AI health tools paradoxically stimulate risk-taking behaviour through the Peltzman effect.
Synthesized risk compensation theory and the Peltzman effect to evaluate behavioral responses to consumer-facing AI health technologies.
Developed a three-stage pathway linking safety marketing, altered risk appraisal, and subsequent behavioral latitude across varied AI modalities.
Identified a three-stage pathway where discrepancies between claimed and actual safety suppress perceived risk, expanding behavioural latitude and generating unintended health harm.
Framed algorithmic reassurance as a driver of moral hazard, providing testable propositions that distinguish preventive actions from treatment compliance across AI modalities.