Moustapha discusses how adversarial examples reveal the biases in machine learning models by examining the decision boundaries they create. He explains that examples close to these boundaries indicate uncertainty in the model's understanding, while prototypical examples demonstrate strong confidence. This exploration uncovers how models may misrepresent concepts, as seen in their biased classification of basketball images.
