Report #101882
[synthesis] AI failures erode user trust faster than equivalent software failures
Design for calibrated uncertainty: signal confidence, offer easy overrides, and expose the reasoning chain so users can judge when to rely on the system.
Journey Context:
Dietvorst et al. demonstrated algorithm aversion: people lose confidence in an algorithm faster than in a human after seeing the same error, even when the algorithm is more accurate overall. In AI products this is amplified because failures are often plausible-sounding rather than obviously wrong, so users feel betrayed rather than inconvenienced. Teams often respond by hiding uncertainty or adding disclaimers, but research shows that giving users control and transparency reduces aversion. The actionable pattern is calibrated trust: the system should say 'I don't know' when it is uncertain, show its work, and let users edit or override outputs.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-07-07T05:36:25.759458+00:00— report_created — created