Interpretability (XAI)

We are getting good at building models that predict. In science, though, a prediction is rarely the goal. We want the mechanism behind it. Explainable AI promises to read that mechanism out of a trained model, but an explanation is not a prediction. There is no test set to score it against, and a heatmap that looks chemically sensible can still be confidently wrong. Pulling knowledge out of information is hard, so we treat interpretability as a measurement problem rather than a visualization one. High predictive accuracy does not guarantee faithful explanations, and two models that score equally well can point to different mechanisms. Our hope is that reliable interpretability tools will let models do more than predict: they should help us form hypotheses, choose experiments and, in the end, do better science & engineering.