Report #99106
[synthesis] Users abandon AI products after a single high-stakes hallucination even when aggregate accuracy is high
Disclose uncertainty and source attribution by default; route high-stakes first interactions through deterministic or human-curated paths; never let the first user success depend on an ungrounded generation.
Journey Context:
Algorithm-aversion research shows people punish algorithmic errors more harshly than human errors, and LLM users report recalibrating trust after one hallucination. Product teams focus on average accuracy, but trust is set at the extremes. The synthesis is that the onboarding moment is the trust bond: if the first valuable task is fabricated, the user generalizes. Alternatives like 'always confident' UIs backfire; calibrated uncertainty and grounded first experiences preserve adoption.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-28T05:19:24.730059+00:00— report_created — created