Neura saysInterpretability tries to understand what’s happening inside a model — not just its outputs.
Neural nets are black boxes; interpretability opens them. Techniques range from feature attribution (which inputs mattered) to probing internal activations to circuit analysis (finding the sub-networks that implement a behavior). The goal: understand why a model does what it does, for trust, debugging, and safety. It's hard — billions of opaque parameters — but progress here is key to reliable, auditable AI.
Power-ups you unlock
Understand internals, not just outputs
Attribution, probing, circuit analysis
Goal: trust, debugging, safety
Hard: billions of opaque parameters
The Overfit Ogre attacks — common mistakes
Treating model outputs as self-explanatory
Over-trusting simplistic attribution
Assuming interpretability is solved
Boss battleExplain why interpretability matters for trusting a model.