让AI解释自己，实现人机协同科学发现与设计优化。
Explainable AI: Learning from the Learners
- 结合因果推理与可解释AI，从AI模型中提取本质规律。
- 支持高风险场景下的可信决策与系统认证。
- 适合科研人员与工程师在复杂系统中深化人机协作。
人工智能在多个科学与工程任务中已超越人类，但其内部表征常不透明。本文主张，将可解释人工智能（XAI）与因果推理结合，可实现‘从学习者中学习’。聚焦于发现、优化与认证，我们展示基础模型与可解释性方法的融合，如何提取因果机制、指导稳健设计与控制，并支撑高风险应用中的信任与问责。文章探讨了解释的忠实性、泛化性与可用性挑战，提出以XAI为统一框架，推动科学与工程领域的人机协同。
原文摘要 · Abstract (English)
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.
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