Trust Calibration

Well-calibrated trust means your confidence in a system roughly matches its actual reliability. If an AI tool is right 80% of the time, you should treat its suggestions as probably correct but worth checking - not as gospel, and not as useless. In practice, almost nobody achieves this naturally. Trust calibration is influenced by how outputs look (polished text feels more trustworthy than it should), how often you use the system (familiarity breeds comfort, not necessarily accuracy), and whether you've ever seen it fail in a way that mattered to you personally. The problem compounds because AI systems rarely come with honest reliability information. Nobody tells you "this model gets this type of question wrong about 30% of the time." Without that information, you're calibrating trust based on vibes rather than evidence. Organisations that want appropriate trust need to actively support calibration - giving users real performance data, showing them examples of where the system struggles, and creating environments where questioning AI outputs is normal rather than seen as being difficult.