Letting you edit an AI's plan can make you feel in control without actually making your trust in it any smarter.

This explores whether a chance to look over an AI's work helps people trust it the right amount, compared with real power over what happens. The corpus suggests the two are less alike than they seem. A review step can make people feel in charge without improving their judgment, while real control mostly changes how people feel about the result. Better-calibrated trust seems to come from somewhere else again.

The clearest evidence comes from a 248-person study where users could edit an LLM agent's plan before it ran Does letting users edit agent plans improve trust calibration?. Being able to edit didn't help people trust good plans and doubt bad ones. Their trust followed how good the plan looked, and plans that read as clear and logical won approval whether or not they were correct. So a review opportunity is only as useful as the reviewer's ability to spot problems, and fluent AI output is good at hiding them. Citations show the same trap in another form. People rate answers with more citations higher, and irrelevant citations raise ratings almost as much as relevant ones Do users trust citations more when there are simply more of them?. A citation looks like something you could check, but most people never check it.

Real control does change things, though not always trust accuracy. When users have more influence over AI-written text, they feel more ownership of it, and personalizing the model doesn't produce that effect Does user control over AI text shape feelings of ownership?. A study of people handing tasks to an agent shows where control matters most. Trust fell sharply on tasks that were irreversible and visible to other people, like sending an email, even when the output was fine. High-stakes tasks that could be corrected didn't cause the same drop What makes people distrust AI agents they delegate to?. What people seem to want is the ability to undo a mistake before others see it, more than a chance to inspect the work. That may explain why only 29% of people in one survey would give an AI agent access to their bank account How willing are people to let AI control their finances?, since money that has been moved is hard to get back.

The design lesson is that more review doesn't automatically mean better oversight. One research-agent system compared full autonomy, step-by-step human approval, and a mode that asked for human input only at uncertain, high-stakes decisions. The targeted mode did best by a wide margin (87.5% accepted vs. 50% and 25%), partly because constant approval requests turn into rubber-stamping Does targeted human oversight beat both full autonomy and exhaustive review?. The format of what people review matters as well. Argument-map explanations improved trust calibration on verbal reasoning tasks but made it worse on visual ones Do visual rationales help or hurt how people calibrate trust?.

The point you may not have expected is that calibrated trust seems to come from feedback over time, not from a single review or a control panel. When people were told a partner was an AI, they avoided it at first, but that bias reversed after repeated interactions where they could see the results. Telling them it was an AI without showing results produced no calibration at all Does revealing AI identity help or hurt user trust?. Review lets you look at the AI's reasoning, and control lets you limit the damage. Appropriate trust depends on seeing what actually happened afterward, again and again. The corpus doesn't yet include a study that tests review, control, and outcome feedback against each other directly, so that comparison is still open.