The answer isn\u2019t just about how AI works<\/a>. It\u2019s about how we work. We don\u2019t understand it, so we don\u2019t trust it. Human beings are more likely to trust systems they understand. Traditional tools feel familiar: you turn a key, and a car starts. You press a button, and a lift arrives.<\/p>\n But many AI systems operate as black boxes: you type something in, and a decision appears. The logic in between is hidden. Psychologically, this is unnerving. We like to see cause and effect, and we like being able to interrogate decisions. When we can\u2019t, we feel disempowered.<\/p>\n This is one reason for what\u2019s called algorithm aversion. This is a term popularised<\/a> by the marketing researcher Berkeley Dietvorst and colleagues, whose research showed that people often prefer flawed human judgement over algorithmic decision making, particularly after witnessing even a single algorithmic error.<\/p>\n We know, rationally, that AI systems don\u2019t have emotions or agendas. But that doesn\u2019t stop us from projecting them on to AI systems. When ChatGPT responds \u201ctoo politely\u201d, some users find it eerie. When a recommendation engine gets a little too accurate, it feels intrusive. We begin to suspect manipulation, even though the system has no self.<\/p>\n This is a form of anthropomorphism \u2013 that is, attributing humanlike intentions to nonhuman systems. Professors of communication Clifford Nass and Byron Reeves, along with others have demonstrated<\/a> that we respond socially to machines, even knowing they\u2019re not human.<\/p>\n We hate when AI gets it wrong<\/p>\n One curious finding from behavioural science is that we are often more forgiving of human error than machine error. When a human makes a mistake, we understand it. We might even empathise. But when an algorithm makes a mistake, especially if it was pitched as objective or data-driven, we feel betrayed.<\/p>\n This links to research on expectation violation<\/a>, when our assumptions about how something \u201cshould\u201d behave are disrupted. It causes discomfort and loss of trust. We trust machines to be logical and impartial. So when they fail, such as misclassifying an image, delivering biased outputs or recommending something wildly inappropriate, our reaction is sharper. We expected more.<\/p>\n The irony? Humans make flawed decisions all the time. But at least we can ask them \u201cwhy?\u201d<\/p>\n Teaching is among the professions where AI is replacing parts of their work. For some, AI isn\u2019t just unfamiliar, it\u2019s existentially unsettling. Teachers, writers, lawyers and designers are suddenly confronting tools that replicate parts of their work. This isn\u2019t just about automation, it\u2019s about what makes our skills valuable, and what it means to be human.<\/p>\n![\"Students](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2025\/11\/file-20251030-56-74ecvd.jpg\")
<\/p>\n
\n BongkarnGraphic \/ Shutterstock<\/a><\/p>\n