{"slug": "ai-and-the-wisdom-of-uncertainty", "title": "AI and the Wisdom of Uncertainty", "summary": "New research shows that people with access to AI-generated answers are less willing to admit \"I don't know,\" even when those answers are wrong, while intellectual humility protects against believing AI-generated misinformation. The findings highlight automation bias and the risk of \"cognitive surrender,\" where users adopt AI outputs with minimal scrutiny, raising concerns about judgment and epistemic resilience.", "body_md": "######\n[Artificial Intelligence](/us/basics/artificial-intelligence)\n\n# AI and the Wisdom of Uncertainty\n\n## Intellectual humility protects judgment and strengthens epistemic resilience.\n\nPosted July 27, 2026\n[\nReviewed by Jessica Schrader\n](/us/docs/editorial-process)\n\n### Key points\n\n- Access to AI answers reduces willingness to admit \"I don't know,\" even when AI is wrong.\n- Intellectual humility shields against believing AI-generated misinformation.\n- Epistemic resilience—tolerating uncertainty—protects judgment and fosters ethical reflection.\n\nNew [research](https://osf.io/preprints/psyarxiv/5y6m4_v1) shows that people with access to AI-generated answers are less willing to admit \"I don't know,\" even when those answers are wrong.\n\nIn the [study](https://osf.io/preprints/psyarxiv/5y6m4_v1), researchers asked participants very specific, obscure questions about films. Participants could always decline to answer and admit they did not know, suspending judgment instead of guessing. The stakes were raised with a monetary penalty for mistakes. But across every version of the experiment, having access to AI advice nearly eliminated participants' willingness to say they did not know, even though AI advice was usually wrong on these questions.\n\nIn another [study](https://arxiv.org/pdf/2606.03377), researchers found that intellectual humility, the metacognitive awareness of one's own limited knowledge, acted as a protective cognitive filter against AI-generated health misinformation. Participants with higher intellectual humility rated pseudoscientific content as less credible.\n\n## The Known Problem of Automation Bias\n\nLongstanding evidence on *automation bias *illustrates\n\n*the human tendency to over-rely on and defer to automated outputs. This has been a concern in*\n\n[medical decision-making](https://ai.nejm.org/doi/full/10.1056/AIoa2501001)and other fields like\n\n[aviation](https://pubmed.ncbi.nlm.nih.gov/11540946/). In the case of AI chatbots, some users may not be aware of their limitations, and may incorrectly perceive and treat automated systems as infallible \"like a calculator\" or like GPS.\n\nLarge language models (LLMs) tend to answer in a fluent, authoritative tone independent of whether the underlying information is reliable. This is highly persuasive, and there is not yet a direct way to gauge the level of certainty behind each answer without independently cross-checking it.\n\nThis is further complicated by the fact that LLMs frequently do provide accurate and helpful information and resources, even though the way they arrive at that information fundamentally [differs](https://arxiv.org/abs/2512.19466) from how humans vet information. This has been termed *epistemia** (*[ episteme](https://plato.stanford.edu/entries/epistemic-paradoxes/) is Greek for knowledge)\n\n*,*which Walter Quattrociocchi and colleagues define as \"a structural situation in which linguistic plausibility substitutes for epistemic evaluation, producing the feeling of knowing without the labor of judgment.\"\n\n## When Should We Apply Our Judgment?\n\nWhile reliance on AI for film questions does not have high stakes, it points to larger questions about our deference toward AI-generated information. I am seeing more patients bringing in AI chatbot opinions of their medical and psychological issues. Some of the information is indeed accurate and useful. But some of it does not quite fit their situations, especially where interpersonal or social judgment is involved.\n\nWharton researchers Shaw and Nave have named this risk of deferring to AI outputs as \"cognitive surrender,\" the concern that people will adopt AI outputs with minimal scrutiny. The trouble is that the devil is in the details.\n\nApplying our own scrutiny and judgment takes effort—and it is not always clear when and how often we should be expending this effort. This challenge is documented in self-driving cars. The [levels](https://www.sae.org/news/blog/sae-levels-driving-automation-clarity-refinements) of driving automation require different degrees of human oversight, and the riskiest zones are the middle levels, where a person is asked to supervise a system that is mostly right and the need to step in is rare. The constant vigilance required and sustained monitoring leads to a decline in [attention](https://www.psychologytoday.com/us/basics/attention), typically within [15 to 30 minutes,](https://www.sciencedirect.com/science/article/abs/pii/S1369847825001147) and cognitive fatigue (”vigilance decrement”) is linked to delayed reaction times.\n\n## Valuing Our Capacity for Uncertainty\n\nThere is a broader issue with the erosion of our ability to hold uncertainty, a capacity British [psychoanalyst](https://www.psychologytoday.com/us/basics/psychoanalysis) Wilfred Bion pointed out as essential, citing poet John Keats's term *negative capability*, the ability to tolerate uncertainty, mystery, doubt, and frustration without rushing to a premature conclusion or \"knowing\" (Bion, 1970).\n\nAs a therapist, I have found that helping people through some of life's most difficult situations often includes helping them expand their capacity to sit with uncertainty—whether with [career](https://www.psychologytoday.com/us/basics/career), relationship, moral, or existential questions, the [anxiety](https://www.psychologytoday.com/us/basics/anxiety) of facing illness, or the inevitable pain of loss and [grief](https://www.psychologytoday.com/us/basics/grief). I see this as a form of *epistemic resilience*—one that is deeply worth cultivating to help us move through difficulty.\n\nThe ability to hold space and resist rushing to judgment is also integral for ethical and moral reflection. Ethicist Sylvie Delacroix [argues](https://link.springer.com/article/10.1007/s11245-026-10392-8) that \"productive uncertainty rather than efficient resolution\" is essential to the infrastructure of ethical transformation. Prematurely foreclosing ethical questions, which can occur when we overtrust and defer to AI responses, prevents us from holding space for uncertainty. We need this space to make difficult discussions generative.\n\n[Artificial Intelligence](https://www.psychologytoday.com/us/basics/artificial-intelligence)Essential Reads\n\n## The Strengths of Epistemic Resilience\n\nIn my practice, I am seeing people express immediate relief, excitement, and even thrill at the expedience of LLM answers, which are quick, concrete, and well-defined. Sometimes it echoes the excitement of getting answers from a psychic or astrologer who offers a blueprint for the future. Both can reduce the short-term anxiety of uncertainty but can come at the cost of narrowing one's own [imagination](https://www.psychologytoday.com/us/basics/imagination) and autonomy. Uncertainty is uncomfortable, but it also gives us the capacity for *superposition*, to hold multiple possibilities at the same time. Our capacity to hold uncertainty allows for an [openness](https://www.psychologytoday.com/us/basics/openness) toward our unknown future, enhancing our agency and self-determination.\n\nThere can also be more insidious relational consequences in automatically deferring to AI. Imagine two partners in the middle of a disagreement over a nuanced quandary. One consults their AI chatbot, asking it to opine. This seems like a neutral third-party, but it is not. This can also result in feelings of betrayal, [anger](https://www.psychologytoday.com/us/basics/anger), loss of trust, and disrupt the mutual feeling of being on the same team, by implying there is more trust in AI than in each other. Repeatedly outsourcing to AI can short-circuit growth through communication and conflict resolution.\n\nNurturing wonder, discomfort, and reflection, without rushing to judgment or conclusions, takes real effort. All this does not mean that we should avoid integrating or relying on helpful AI systems. But in hybrid human-AI systems, it is important to consider not only short-term ease but also the long-term benefits of cultivating our capacity to handle uncertainty and epistemic humility.\n\nCopyright Marlynn Wei, MD, JD © Copyright 2026. All Rights Reserved.\n\nReferences\n\nDelacroix, Sylvie. “Structural Uncertainty and the Conditions of Transformative Agency.” *Topoi*, ahead of print, March 12, 2026. [https://doi.org/10.1007/s11245-026-10392-8](https://doi.org/10.1007/s11245-026-10392-8).\n\nMarcoccia, Chiara, Walter Quattrociocchi, and Valerio Capraro. “AI Advice Suppresses People’s Willingness to Say “I Don’t Know”, Even When the Advice Is Wrong and Accuracy Is Incentivized”. PsyArXiv, July 15, 2026. osf.io/preprints/psyarxiv/5y6m4_v1.\n\nQuattrociocchi, Walter, Valerio Capraro, and Matjaž Perc. “Epistemological Fault Lines Between Human and Artificial Intelligence.” arXiv.Org, December 22, 2025. [https://arxiv.org/abs/2512.19466v1](https://arxiv.org/abs/2512.19466v1).\n\nRządeczka, Marcin, Maciej Wodziński, Kacper Zacharski, and Marcin Moskalewicz. “Intellectual Humility as a Cognitive Filter for AI-Generated Health Misinformation. An Evolutionary Perspective on Epistemic Vigilance.” arXiv:2606.03377. Preprint, arXiv, June 10, 2026. [https://doi.org/10.48550/arXiv.2606.03377](https://doi.org/10.48550/arXiv.2606.03377).\n\nShaw, Steven D and Nave, Gideon, Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender (January 11, 2026). [https://doi.org/10.31234/osf.io/yk25n_v1](https://doi.org/10.31234/osf.io/yk25n_v1), The Wharton School Research Paper , Available at SSRN: [https://ssrn.com/abstract=6097646](https://ssrn.com/abstract=6097646) or [http://dx.doi.org/10.2139/ssrn.6097646](https://dx.doi.org/10.2139/ssrn.6097646)", "url": "https://wpnews.pro/news/ai-and-the-wisdom-of-uncertainty", "canonical_source": "https://www.psychologytoday.com/us/blog/urban-survival/202607/ai-and-the-wisdom-of-uncertainty", "published_at": "2026-07-27 13:11:08+00:00", "updated_at": "2026-07-27 13:35:46.818196+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-ethics", "ai-safety", "large-language-models"], "entities": ["Walter Quattrociocchi", "Wharton", "Shaw", "Nave"], "alternates": {"html": "https://wpnews.pro/news/ai-and-the-wisdom-of-uncertainty", "markdown": "https://wpnews.pro/news/ai-and-the-wisdom-of-uncertainty.md", "text": "https://wpnews.pro/news/ai-and-the-wisdom-of-uncertainty.txt", "jsonld": "https://wpnews.pro/news/ai-and-the-wisdom-of-uncertainty.jsonld"}}