Artificial intelligence assistants can produce polished explanations within seconds, even when the information behind them is incomplete or incorrect. Their organized structure, confident language and immediate availability may give users the impression that an answer has been carefully verified.
The danger is not limited to receiving false information. An AI response may also increase a person’s confidence in that information, making the error less likely to be questioned or checked elsewhere.
Research on human–AI interaction suggests that people often struggle to judge when a model is right. Longer explanations, algorithmic labels and visual justifications can all influence confidence without reliably improving accuracy.
Fluent Explanations Can Hide Uncertainty
Large language models generate text by predicting likely sequences of words. They do not automatically verify every statement against a trusted database before presenting it. However, the smoothness of their language can make uncertain conclusions sound settled.
A 2025 study published in Nature Machine Intelligence found that users generally overestimated the accuracy of answers produced by GPT-3.5, PaLM 2 and GPT-4o when they were shown the models’ standard explanations.
The researchers identified a “calibration gap” between how accurate people believed the models were and how often their answers were actually correct. They also found a “discrimination gap,” meaning participants had difficulty separating likely correct responses from likely incorrect ones based on the explanations alone.
The models’ internal confidence distinguished correct from incorrect answers reasonably well, producing area-under-the-curve scores of 0.751 for GPT-3.5, 0.746 for PaLM 2 and 0.781 for GPT-4o. Participants interpreting the models’ default explanations achieved much weaker scores of 0.589, 0.602 and 0.592, respectively—only slightly better than chance.
The finding reveals an important communication failure. A model may possess some internal signal that its answer is unreliable, yet the language delivered to the user may not communicate that uncertainty effectively.
Longer Answers Can Appear More Trustworthy
People often associate detail with expertise. A long response may seem more carefully reasoned because it contains examples, definitions and step-by-step explanations. Yet additional words do not necessarily add evidence.
In the same Nature Machine Intelligence study, longer AI explanations increased participants’ confidence even though the added length did not improve their ability to distinguish accurate answers from inaccurate ones.
Across the experiments, participants agreed with the model’s selected response in 82% of the multiple-choice cases. When users departed from the model’s answer, their accuracy did not consistently improve because they often lacked enough independent subject knowledge to correct it.
This creates a persuasive imbalance. The AI can generate a complete explanation quickly, while the user may need substantial time and expertise to evaluate each factual link in its reasoning.
A detailed answer can therefore function as a confidence signal even when the details are irrelevant, circular or built on an incorrect premise.
Explanations Do Not Automatically Prevent Overreliance
Making AI more explainable is often presented as a solution. The assumption is that showing why a system reached a conclusion will help users identify errors. Experimental evidence suggests that this does not always happen.
A Scientific Reports study conducted five preregistered experiments involving 1,403 students and human-resources employees who received correct or incorrect advice during personnel-selection tasks.
Participants frequently followed inaccurate recommendations, reducing their decision performance, and adding visual explanations such as heatmaps or charts did not reliably help them recognize the mistakes.
In one experiment, explanations attached to incorrect AI advice increased participants’ confidence above the level recorded when they received no advice, even though the explanations did not improve performance.
An explanation can make an answer easier to understand without making it true. When the explanation is coherent, users may evaluate its presentation rather than checking whether its assumptions and evidence are valid.
The Algorithm Label Can Increase Confidence
People do not always treat algorithmic and human advice equally. In some contexts, simply believing that a recommendation came from a computational system can increase its influence.
A Scientific Reports experiment asked participants to solve 18 word-association problems before receiving advice described either as the output of an algorithm or as the consensus of other people.
Across 2,772 responses, participants changed their initial answers 31% of the time after algorithmic advice, compared with 18% after advice attributed to other people.
Greater compliance did not produce better results. Participants receiving algorithmic advice reached the correct final answer in 66.54% of cases, compared with 79.66% among those receiving advice presented as coming from humans.
The algorithm group nevertheless reported higher confidence in its final answers, and low-quality algorithmic advice produced confidence comparable to correct advice attributed to people.
This pattern resembles automation bias—the tendency to give disproportionate weight to automated recommendations, especially when users assume a machine has processed more information than they could examine themselves.
AI Systems Can Seem More Confident Than Humans
The appearance of confidence may exist even when an AI and a person behave identically.
A 2026 Communications Psychology paper reported seven preregistered experiments in which participants observed human and AI agents making perceptual or general-knowledge decisions.
Observers consistently judged the AI agents as more confident than humans even when their accuracy, response times and other visible behaviors were the same. The researchers linked this effect partly to prior beliefs about machines’ capabilities.
Users may therefore supply confidence that the system itself never explicitly expressed. Expectations that computers are consistent, objective or mathematically precise can shape how an otherwise neutral response is interpreted.
Better Uncertainty Language Can Reduce the Risk
Warnings alone do not solve the problem, but the way uncertainty is communicated can affect user behavior.
A preregistered experiment involving 404 participants examined responses to medical questions presented through a fictional search engine powered by a large language model.
When answers began with first-person uncertainty language such as “I’m not sure, but,” participants expressed less confidence in the system, agreed with it less frequently and achieved higher accuracy. The researchers attributed the improvement to reduced—but not eliminated—acceptance of incorrect AI answers.
The study remains a preprint, but it supports a practical principle: AI assistants should not present every response with the same polished certainty. Uncertainty needs to be visible precisely when the model has weak evidence or limited knowledge.
Confidence Should Follow Verification
Users can reduce overreliance by separating the quality of an explanation from the quality of its evidence. Important claims should be checked against original studies, official records or established expert sources rather than accepted because an answer is detailed.
AI assistants should also be asked to identify uncertainty, distinguish fact from inference and provide sources that users can open themselves. For consequential medical, legal, financial or professional decisions, AI output should remain a starting point rather than the final authority.
The central risk is not that people will believe every incorrect answer. It is that AI can make uncertainty feel like knowledge and make borrowed conclusions feel personally verified.
As assistants become more fluent and personalized, the most valuable human skill may be knowing when confidence has been earned—and when it has merely been generated.
