mlsanityData to 7 Oct 2026

AI advice suppresses people's willingness to say "I don't know", even when the advice is wrong and accuracy is incentivized

arXiv 2607.13562 ↗First posted 15 Jul 2026cs.AI cs.CY cs.HCcited by 2

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Abstract (arXiv)

Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments (N = 3,132; four preregistered, one direct replication), participants answered difficult questions and could always decline to respond. We engineered the questions so that AI advice was wrong, separating AI use from its accuracy. Merely having access to AI nearly eliminated participants' willingness to suspend judgment, and this held whether the advice was actively requested or simply displayed. Consequently, participants answered more questions but were correct about a third as often as when AI was unavailable-yet their confidence nearly doubled. Incentivizing accuracy and penalizing inaccuracy led participants to seek and follow AI advice less, answer more accurately, and suspend judgment more often, though still far less than when AI was unavailable. As AI suggestions grow ubiquitous and unsolicited, they may not simply affect answer accuracy; they may even alter the metacognitive threshold at which people decide whether they know enough to answer.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 15 Jul 2026.

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9 of 43 references.

References · 9

  1. 2024On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trialcited by 105
  2. 2023AI model GPT-3 (dis)informs us better than humanscited by 75
  3. 2025The simulation of judgment in LLMscited by 22
  4. 2023The impact of generative artificial intelligence on socioeconomic inequalities and policy makingcited by 20
  5. 2024Epistemic Injustice in Generative AIcited by 16
  6. 2025When Large Language Models are More PersuasiveThan Incentivized Humans, and Whycited by 11
  7. 2025Epistemological Fault Lines Between Human and Artificial Intelligencecited by 11
  8. 2026LLMorphism: When humans come to see themselves as language modelscited by 1
  9. 2026Profiling cognitive offloading in LLM-mediated synthesis writing: Volume vs. contentcited by 1

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