CNN Interpretability
A model that's 98% accurate on your test set can still be completely wrong about why. A famous, real example: a classifier trained to distinguish huskies from wolves turned out to be detecting snow in the background, not the animal — accurate on a test set that happened to reflect the same correlation, and silently broken the moment that correlation didn't hold. Interpretability tools exist to catch exactly this before deployment, not after.