Making AI explainable without peeking inside the black box

Instead of opening up a locked safe to see what's inside, you're learning what the safe cares about by strategically covering different parts of it and watching its behavior change.

This means you can finally understand *why* an image-recognition AI made a decision—without needing access to its internal weights or millions of labeled training examples.