How well do explanation methods for machine-learning models work?

Imagine a team of physicians using a neural network to detect cancer in mammogram images. Even if this machine-learning model seems to be performing well, it might be focusing on image features that are accidentally correlated with tumors, like a watermark or timestamp, rather than actual signs of tumors.

To test these models, researchers use “feature-attribution methods,” techniques that are supposed to tell them which parts of the image are the most important for the neural network’s prediction. But what if the attribution method misses features that are important to the model? Since the researchers don’t know which features are important to begin with, they have no way of knowing that their evaluation method isn’t effective.

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