…When blue dots became rare, purple dots began to look blue; when threatening faces became rare, neutral faces began to appear threatening; and when unethical research proposals became rare, ambiguous research proposals began to seem unethical. This happened even when the change in the prevalence of instances was abrupt, even when participants were explicitly told that the prevalence of instances would change, and even when participants were instructed and paid to ignore these changes.
Here is a public policy implication:
… When strong sexism declines, for example, … what was once not considered sexism at all (e.g. “men and women have different preferences which might explain job choice“) now becomes violently sexist.
I am thinking about how this kind of bias might appear in a business setting. For example, as particular kinds of accidents become less prevalent (rarer), if consumers overestimate the probability of these accidents, then consumers will be willing to pay much more than the cost of the insurance [probability of an accident times the cost of an accident].