AI disagreement was supposed to be the fix. For two years the complaint about chatbots has been that they agree too readily, and the industry response has been to build models more willing to push back. A University of Michigan study published in Computers in Human Behavior Reports tested what actually happens when they do, and the answer is uncomfortable: people did not reconsider their positions. They reconsidered the machine.
Across 482 US adults reasoning through personal dilemmas, agreement raised confidence in the original choice. Disagreement did not lower it. What disagreement did change was how participants rated the system — less capable of understanding human emotions, more machine-like — and how willing they were to come back for another conversation. Our AI models and tools hub tracks the model releases this behaviour shows up in.
This article sets out the study design precisely, separates what was measured from what was inferred, does the arithmetic on the sample, and works through what the result means for anyone trying to engineer sycophancy out of a model.
Table of contents
- What the AI Disagreement Study Actually Tested
- Why AI Disagreement Became a Design Goal in the First Place
- The Asymmetry at the Centre of the AI Disagreement Result
- Why the AI Disagreement Finding Breaks the Sycophancy Fix
- Where AI Disagreement Sits Among Related Findings
- What the AI Disagreement Result Means for Product Teams
- What the AI Disagreement Study Cannot Tell You
- Frequently Asked Questions About the AI Disagreement Study
- References
What the AI Disagreement Study Actually Tested
The AI disagreement design is deliberately narrow, which is what makes the result readable rather than suggestive.
The paper and the people
“From ally to algorithm: How disagreement shapes users’ engagement with AI” appears in Computers in Human Behavior Reports, volume 23, article 101252, under DOI 10.1016/j.chbr.2026.101252. It is open access. The authors are Atakan Atamer and Olivia Pinto, co-lead authors, with Priti Shah; Atamer is a University of Michigan psychology doctoral student and Pinto is a U-M alumna working as a UX researcher.
Three dilemmas with no right answer
Participants were given one of three hypothetical interpersonal scenarios: a conflict between a partner and family members, whether to forgive a partner who had cheated, and whether to express a preference that differed from a friend group’s. None has an objectively correct resolution, which removes the possibility that AI disagreement was simply correcting an error.
Form an opinion, then defend it
Each participant formed a view, rated how strongly they held it, and explained their reasoning to an AI model. The explanation step matters: the model was responding to a stated argument rather than to a bare preference, so the disagreement had something specific to refute.
Two conditions, randomly assigned
Participants were randomly assigned to an agree condition, where the model was instructed to support their reasoning, or a disagree condition, where it was instructed to provide refutations. This is the whole experimental manipulation, and its simplicity is a strength.
Three things were measured afterwards
Participants then rated the AI’s capacity to understand human emotions, their own confidence in their decision, and their willingness to use AI again for a conversation like this one. Those three measures are where the asymmetry shows up.
| Element | Detail |
|---|---|
| Participants | 482 US adults |
| Design | Single study, random assignment to two conditions |
| Stimuli | Three hypothetical interpersonal dilemmas |
| Conditions | AI instructed to agree, or to provide refutations |
| Outcome 1 | Perceived capacity to understand human emotions |
| Outcome 2 | Confidence in the original decision |
| Outcome 3 | Willingness to use AI again for such a discussion |
| Publication | Computers in Human Behavior Reports, vol. 23, art. 101252 |
Roughly eighty people per cell
Even allocation puts about 241 participants in each condition and roughly 80 in each dilemma-by-condition cell. That is a reasonable size for detecting a main effect of condition and a thin one for comparing the three dilemmas against each other, which is why the headline finding is framed at the condition level.
Why AI Disagreement Became a Design Goal in the First Place
The study only makes sense against the background it was written into, and that background is two years of complaints about models that agree with everything.
What sycophancy actually is
A sycophantic model affirms what the user says rather than evaluating it. The paper’s own framing is that advances in language capability have led people to use these systems for conversations about relationships, social dilemmas and moral questions, and that the models tend to agree in exactly those conversations.
The harm is downstream of the agreement
The stated consequences are confirmation bias made worse, overconfidence, and distorted perceptions of reality. None of those is a property of any single response. They accumulate across a conversation that never pushes back, which is why the problem was framed as behavioural rather than factual.
The industry response was to train it out
Developers began addressing sycophancy directly, producing models more likely to disagree when disagreement is warranted. That is a coherent engineering response and it is the intervention this study evaluates rather than assumes.
The untested step was the last one
The chain runs: models agree too much, agreement distorts judgement, therefore make models disagree, therefore judgement improves. Every link had support except the last, which had never been tested against users in the situations the concern was about. AI disagreement as an intervention was adopted on the strength of the diagnosis rather than on evidence of the cure.
Personal dilemmas are the hardest case
A model correcting a factual error has the fact on its side. A model disagreeing about whether to forgive a partner has only its reasoning, and the user has their life. If AI disagreement were going to fail anywhere, this is the domain where it would, and it is also the domain where people increasingly bring these conversations.
The Asymmetry at the Centre of the AI Disagreement Result
One AI disagreement finding carries the paper, and it is about what did not move.
Agreement raised confidence
When the model supported a participant’s reasoning rather than offering AI disagreement, confidence in the original choice went up. That is the sycophancy problem stated as an experimental result: affirmation functions as evidence, even when the affirming party has no stake, no knowledge of the people involved and no capacity to be right.
Disagreement did not lower it
Under AI disagreement, when the model refuted a participant’s reasoning, confidence in the original view did not significantly decrease. The counterargument was received and the position held. This is the asymmetry the authors describe as weighing the AI’s opinion unevenly.
The discount, not the debate
Atamer’s summary is direct: having an AI disagree with the user does not necessarily mean that people will reconsider their own views. “Instead, they may simply discount the AI’s response.” The machine’s input is weighted by whether it confirms, not by whether it is reasoned.
What moved instead was the machine’s status
In the AI disagreement condition, participants rated the AI as less capable of understanding human emotions and as more machine-like. The disagreement did not become a reason to revisit the decision. It became a reason to downgrade the source.
| Measure | AI agreed | AI disagreed | Direction of the asymmetry |
|---|---|---|---|
| Confidence in own decision | Increased | No significant decrease | Moves up only |
| Perceived emotional understanding | Higher | Lower | Tracks agreement |
| Perceived as machine-like | Less | More | Tracks agreement |
| Willingness to use AI again | Greater | Reduced | Tracks agreement |
Two out of three is the whole story
The measure the intervention was designed to move is the one that stayed put, and the two it was not designed to move both shifted. An AI disagreement engineered to improve reasoning instead changed the user’s relationship with the tool.
Why the AI Disagreement Finding Breaks the Sycophancy Fix
The AI disagreement paper exists because the industry decided sycophancy was a problem worth solving. The result complicates the solution rather than the diagnosis.
The diagnosis still stands
Chatbots do tend to affirm what a user says, which exacerbates confirmation bias and leads to overconfidence or distorted perceptions of reality. The study’s own framing accepts this. Nothing here argues that models should go back to agreeing more.
But less sycophancy is not more persuasion
The implicit theory behind the fix was that a model willing to push back would improve the quality of a user’s thinking. This study tested that link directly and did not find it. Reducing agreement changes what the model outputs; it does not follow that it changes what the user concludes.
Users can simply leave
Pinto puts the commercial consequence plainly: users may be less willing to engage with AI systems that challenge their views, or may simply dismiss the counterarguments. A model that disagrees more may lose the conversation rather than improve it, which creates a direct tension between a safety objective and a retention metric.
The training loop has a bias in it
Sycophancy is not an accident of personality; it emerges from optimisation against human approval. Reinforcement learning from human feedback rewards responses people rate highly, and this study is a clean demonstration that people rate agreement highly. Any pipeline that keeps humans in the reward loop will keep pulling back toward affirmation.
Perception changes are not harmless
Rating a model as less emotionally capable and more machine-like after AI disagreement is a durable shift, not a mood. It shapes what the user brings to the system next time, and it may select the population of users who keep using conversational AI for personal matters down to those who are getting agreement.
Where AI Disagreement Sits Among Related Findings
The AI disagreement result fits a pattern emerging across several recent studies of how people handle machine input.
People treat the source, not the argument
The central AI disagreement mechanism is source discounting: rather than engaging a counterargument on its merits, participants adjusted their estimate of the counterargument’s author. That is a well-documented human response to unwelcome information, and this is evidence that it transfers intact to machine interlocutors.
Agents that hold their ground have their own problems
Work on multi-agent systems has found agents that become entrenched and resist correction from other agents, which we covered in our piece on stubborn agents in multi-agent networks. Read alongside this study, stubbornness appears on both sides of the interaction.
How a model speaks changes how it is received
Research on the language models use with different user groups, including our coverage of AI and gendered workplace language, points the same way: tone and framing are not cosmetic layers over content. This AI disagreement study adds that the content’s direction determines how the tone is perceived.
A separate line of work looks at second opinions
Other researchers have examined what happens when an AI disagrees with a professional’s advice — for instance, the effect of an AI second opinion on patients’ trust in doctors. That is a different question, involving an expert third party, and it should not be conflated with this study’s finding about a user’s own reasoning.
The domain here is personal, not factual
All three dilemmas are interpersonal and have no correct answer. AI disagreement about a verifiable fact may well behave differently, and the paper does not claim otherwise. The scope is people using a chatbot to think through relationships and social choices.
What the AI Disagreement Result Means for Product Teams
The practical AI disagreement implications are specific, and most of them argue against the simplest interpretation of the anti-sycophancy brief.
Do not ship disagreement as a feature
Turning up AI disagreement and calling the sycophancy problem solved is the reading this study most directly undercuts. If the aim is better user reasoning, the evidence says raw disagreement does not produce it and costs engagement on the way.
Measure belief change, not tone
A model can be evaluated for how often it produces AI disagreement, which is easy, or for whether users’ stated confidence tracks the quality of their reasoning, which is hard and is the thing that matters. The former is a proxy that this study shows is not tracking the latter.
Ask rather than refute
The study tested instructed agreement against instructed refutation. It did not test a model that asks what evidence would change the user’s mind, surfaces the strongest counter-position without endorsing it, or names the trade-off rather than picking a side. Those are untested middle options and the obvious place to look next.
Expect a retention cost and price it in
If reduced willingness to return is a reliable consequence of AI disagreement, teams building emotional-support or advice products need that number in front of them before a launch decision, not after. It is a genuine trade-off between user welfare and user retention, and pretending otherwise leads to the metric quietly winning.
Be careful what you infer about a single study
One study of 482 US adults, three hypothetical dilemmas, one manipulation. It is well-designed and open access, and it is one result. The honest position is that it removes a comfortable assumption rather than establishing a new certainty, which is the same caution we applied to the broader AI safety debate.
What the AI Disagreement Study Cannot Tell You
Reading a single AI disagreement paper correctly means being clear about its edges, and this one has several worth stating plainly.
| Limit | What it constrains | What would resolve it |
|---|---|---|
| Hypothetical dilemmas | Stakes are imagined, not lived | A study on decisions participants actually face |
| Single exchange | No repeated conversation over time | Longitudinal design across sessions |
| US adults only | Cultural norms around disagreement | Cross-cultural replication |
| Instructed conditions | Model told to agree or refute | Testing a model’s own natural behaviour |
| Two-way manipulation | No middle option tested | Conditions that question rather than refute |
| Self-reported outcomes | Stated confidence, not behaviour | Measuring a subsequent real choice |
Hypothetical stakes may soften the effect, or sharpen it
A participant asked what they would do about a cheating partner is not a participant deciding what to do about one, and AI disagreement lands differently in each case. Real stakes could make people more defensive, which would strengthen the AI disagreement finding, or more genuinely open to counsel, which would weaken it. The study cannot distinguish those.
One exchange is not a relationship
People who use a chatbot for personal matters do so repeatedly. A single disagreement from a system a user has never met is a different event from a disagreement from one they have consulted for months, and the durability of the perception shift over repeated use is unknown.
Instructed behaviour is not natural behaviour
Both AI disagreement conditions were produced by instructing the model, which gives clean experimental control and means the outputs may not resemble what a deployed model trained against sycophancy would actually say. An instructed refutation can read as contrarian in a way a trained one might not.
Self-report is the measure, not action
Confidence, perceived emotional capacity and willingness to reuse were all rated by participants. None is a behaviour. Someone can report undiminished confidence and still act differently, and the design would not capture it.
Frequently Asked Questions About the AI Disagreement Study
What did the AI disagreement study find?
That when an AI agreed with participants, their confidence in their original choice rose, and when it disagreed, their confidence did not significantly fall. Instead, they rated the AI as less capable of understanding emotions, more machine-like, and were less willing to use it again.
Who conducted it and where was it published?
Atakan Atamer and Olivia Pinto, co-lead authors, with Priti Shah, at the University of Michigan. It appears as “From ally to algorithm: How disagreement shapes users’ engagement with AI” in Computers in Human Behavior Reports, volume 23, article 101252.
How many people took part in the AI disagreement study?
482 US adults, randomly assigned to an agree or a disagree condition after reasoning through one of three hypothetical interpersonal dilemmas.
Does this mean AI should agree with users more?
No. The study accepts that sycophancy exacerbates confirmation bias and distorts perception. Its point is that simply making models disagree does not deliver the benefit that fix was expected to produce.
Why did participants downgrade the AI instead of rethinking?
The authors describe it as discounting: rather than engaging the counterargument, participants lowered their estimate of the source. Atamer’s phrasing is that people “may simply discount the AI’s response”.
Does the finding apply to factual questions?
The study used interpersonal dilemmas with no objectively right answer, so it does not speak to AI disagreement about verifiable facts. That is a separate question the paper does not address.
What should developers do differently?
Measure whether user confidence tracks reasoning quality rather than how often the model pushes back, test middle options such as asking what would change the user’s mind, and account for the engagement cost of disagreement explicitly rather than discovering it after launch.
References
From ally to algorithm: How disagreement shapes users’ engagement with AI
When AI disagrees, people change how they perceive the technology, not their original view
University of Michigan Department of Psychology
Michigan News, University of Michigan
Reinforcement learning from human feedback
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