Does knowing more about AI, or about your own job, actually protect you from blindly trusting its suggestions?

This explores whether knowing more, either about how AI works or about your own professional domain, makes you less likely to defer to a machine's suggestion when you shouldn't. The short answer from the corpus is that knowledge helps, but less reliably than you'd hope. The collection also suggests that expertise can wear away under the very automation it is supposed to check. One caveat first: the library has no study that directly compares AI-literate users with novices. The picture below is pieced together from adjacent work.

The clearest link between knowledge and vulnerability comes from a review of 35 studies on automation bias How do anchoring and Dunning-Kruger deepen AI overreliance?. It finds that automation bias doesn't act alone. It stacks with anchoring, where the AI's first suggestion becomes the reference point you adjust from, and with the Dunning-Kruger effect, where people with little domain knowledge can't tell when the AI is wrong because spotting the error takes the very expertise they lack. So low knowledge does more than leave you passive. It takes away the tools you'd need to notice a problem at all. Anchoring is the warning for experts, though: it can pull a knowledgeable person's judgment toward the AI's first answer before their expertise kicks in.

The more surprising thread is that expertise is not a fixed shield. A meta-analysis of 18 automation experiments When does automation harm performance after failures? found that automation improves routine performance but makes recovery from failures worse, and the harm is sharpest when the system moves from analyzing information to choosing actions. Operators who watch an automated decision-maker gradually lose the hands-on skill and awareness they would need to take over. Professional expertise, in other words, can quietly decay through exactly the kind of oversight role it is assigned. When things then go wrong, the human is still blamed: research on 'moral crumple zones' Do automated systems shift blame onto their human operators? shows investigators assign responsibility to the nearby operator regardless of how much control or information they actually had.

AI literacy runs into a different problem: some of the pull toward over-trust works below the level of what you know. Users in every language studied follow confidence signals rather than accuracy, so overconfident wrong answers get followed systematically Do users worldwide trust confident AI outputs even when wrong?. The Rose-Frame work Why do people trust AI outputs they shouldn't? describes three traps that reinforce one another. People mistake fluent text for grounded knowledge, treat quick intuition as careful reasoning, and get their existing beliefs confirmed. These are cognitive habits, not knowledge gaps, so understanding transformers doesn't switch them off. A related piece How do competent systems quietly undermine safety oversight? argues that the most dangerous systems are the ones that look competent, because fluent output wears down the skepticism a knowledgeable user would otherwise apply.

There is also a social side you might not expect. In experiments with more than 4,400 people, AI users expected colleagues to rate them as less competent and were reluctant to disclose their AI use Do people fear judgment when they use AI at work?. If professionals hide their AI use, it escapes the peer review and second opinions that usually catch errors in expert work. The takeaway: knowing your field protects you against obvious AI mistakes, but it doesn't protect you from fluency, confidence cues, skill decay, or the urge to use AI quietly. Those risks depend more on how the work is set up than on what any one person knows.