Explainable AI: Learning from the Learners
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917274276855808 |
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| author | Vinuesa, Ricardo Brunton, Steven L. Mengaldo, Gianmarco |
| author_facet | Vinuesa, Ricardo Brunton, Steven L. Mengaldo, Gianmarco |
| contents | 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), combined with causal reasoning, enables {\it learning from the learners}. Focusing on discovery, optimization and certification, we show how the combination of foundation models and explainability methods allows the extraction of causal mechanisms, guides robust design and control, and supports trust and accountability in high-stakes applications. We discuss challenges in faithfulness, generalization and usability of explanations, and propose XAI as a unifying framework for human-AI collaboration in science and engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05525 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Explainable AI: Learning from the Learners Vinuesa, Ricardo Brunton, Steven L. Mengaldo, Gianmarco Artificial Intelligence Machine Learning Computational Physics Physics and Society 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), combined with causal reasoning, enables {\it learning from the learners}. Focusing on discovery, optimization and certification, we show how the combination of foundation models and explainability methods allows the extraction of causal mechanisms, guides robust design and control, and supports trust and accountability in high-stakes applications. We discuss challenges in faithfulness, generalization and usability of explanations, and propose XAI as a unifying framework for human-AI collaboration in science and engineering. |
| title | Explainable AI: Learning from the Learners |
| topic | Artificial Intelligence Machine Learning Computational Physics Physics and Society |
| url | https://arxiv.org/abs/2601.05525 |