Explainable AI: learning from the learners.

Authors

Ricardo Vinuesa, Steven L Brunton, Gianmarco Mengaldo

Published in

Nature communications. Volume 17. Issue 1. Aug 06, 2026. Epub Aug 06, 2026.

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.

PMID:
42562819
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.

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