Explainable AI: learning from the learners.

Abstract

Artificial intelligence now outperforms humans in several scientific and engineering tasks, yet its internal representations often remain opaque. In this Perspective, we argue that explainable artificial intelligence (XAI), used alongside causal reasoning and domain validation, enables learning from the learners. Focusing on discovery, optimization and certification, we show how foundation models and explainability methods can expose model-internal decision processes, generate candidate mechanistic hypotheses, guide robust design and control, and support trust and accountability in high-stakes applications.