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[2601.05525] Explainable AI: Learning from the Learners
Summary
This article discusses the importance of explainable AI (XAI) in enhancing trust and accountability in AI applications, particularly in scientific and engineering contexts. It emphasizes the integration of causal reasoning with XAI to improve understanding and usability of AI ...
Why It Matters
As AI systems increasingly outperform humans in various tasks, understanding their decision-making processes becomes crucial. This article highlights how XAI can facilitate better human-AI collaboration, ensuring that AI applications are reliable and transparent, especially in high-stakes environments.
Key Takeaways
- Explainable AI (XAI) enhances trust in AI systems by clarifying decision-making processes.
- Integrating causal reasoning with XAI can improve the usability and effectiveness of AI applications.
- XAI serves as a framework for better collaboration between humans and AI in scientific and engineering fields.
- Addressing challenges in explanation faithfulness and generalization is crucial for effective XAI.
- The combination of foundation models and explainability methods can extract causal mechanisms for robust design.
Computer Science > Artificial Intelligence arXiv:2601.05525 (cs) [Submitted on 9 Jan 2026 (v1), last revised 14 Feb 2026 (this version, v2)] Title:Explainable AI: Learning from the Learners Authors:Ricardo Vinuesa, Steven L. Brunton, Gianmarco Mengaldo View a PDF of the paper titled Explainable AI: Learning from the Learners, by Ricardo Vinuesa and 2 other authors View PDF HTML (experimental) 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), 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. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Physics (physics.comp-ph); Physics and Society (physics.soc-ph) Cite as: arXiv:2601.05525 [cs.AI] (or arXiv:2601.05525v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2601.05525 Focus to learn more arXiv-issued DOI via DataCite Submission h...
