The article discusses the base rate fallacy, a cognitive bias affecting both humans and AI models, where the overall probability of an event is overlooked in favor of new evidence. Examining a scenario involving a disease with a rare occurrence, the text highlights how a test boasting 99% accuracy can still yield a low probability (9%) of actually having the disease upon a positive test result. This illustrates that without considering the base rate (1 in 1000), data interpretation can lead to misguided conclusions, shedding light on the importance of context in statistical assessments.
Despite a test that's '99% accurate,' your chance of being sick is only 9%, because the disease is so rare. That 1-in-1000 is the base rate.
The base rate fallacy is a bias that causes both humans and machines to misjudge probabilities when we overlook the context in which data exists.
When we evaluate probability, we subconsciously replace hard questions with easier ones, focusing on how well a situation matches our mental stereotypes.
Ignoring base rates can lead to massive misinterpretation of data, causing a confident but inaccurate assessment of a situation based on flawed assumptions.
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