Nature Communications Perspective: Explainable AI and Foundation Models for Scientific Discovery
ricardovinuesa · x · 2026-08-07
A new Perspective published in Nature Communications titled "Explainable AI: learning from the learners" by Ricardo Vinuesa and colleagues.
The authors argue that while AI outperforms humans in many scientific tasks, its internal representations remain opaque. They propose that combining Explainable AI (XAI) with causal reasoning and domain validation enables us to "learn from the learners."
Specifically, 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.
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