CONCEPT
Base-Rate Neglect
The cognitive bias of ignoring prior probabilities in favor of case-specific information — a systematic error Tetlock documented in expert forecasters and that AI's narrative fluency amplifies.
Base-rate neglect is the tendency to underweight or ignore the statistical frequency of an outcome in a reference class when making predictions about a specific case. Physicians estimating the probability that a patient with a positive test result actually has a disease often ignore the base rate of the disease in the population, focusing instead on the test's sensitivity. Forecasters predicting whether a specific technology will be transformative often ignore the base rate of transformative-technology predictions coming true, focusing instead on the technology's impressive features. Tetlock's research demonstrated that experts are particularly vulnerable to base-rate neglect because their domain knowledge provides rich case-specific details that overwhelm the austere discipline of considering the prior probability.
In The You On AI Field Guide
The canonical demonstration involves a screening test with ninety-five-percent sensitivity (correctly identifies ninety-five percent of sick patients) and five-percent false-positive rate, applied to a population where the disease prevalence is one in a thousand. A positive result has only a two-percent probability of indicating actual disease, because