面向可靠工业人工智能的结构对齐：连接物理现实、数据、模型与人类意图
Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent
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中文总结 AI 辅助
该框架将工业AI可靠性视为物理、表征、机器和人类认知四个领域间的结构对齐问题，通过数字化和目标编码接口定义解空间，并识别出不同领域失败模式背后的共同结构机制，为系统级可靠性评估提供原则性基础。
中文摘要 AI 辅助
人工智能正越来越多地部署于关键工业领域，包括医疗保健、能源电网、地下勘探等，在这些领域中，失败可能对人类安全、系统稳定性和经济结果造成严重后果。然而，人工智能目前仍主要通过基准测试准确率进行评估，这是一种以模型为中心的指标，无法捕捉真实世界部署的结构复杂性和风险。我们提出了一个框架，将工业人工智能的可靠性视为四个相互作用的领域之间的结构对齐问题：物理领域、表征领域、机器领域和人类认知领域。这些领域通过两个接口连接：数字化，将物理现实连接到计算表征；以及目标编码，将人类认知转化为机器目标。它们共同定义了可行解的空间。我们通过四个属性来表征解空间：存在性、非唯一性、鲁棒性和可解释性，并展示了不匹配如何在接口处产生并跨领域传播，从而导致可靠性失败。在医疗保健、能源电网和地下勘探中的应用表明，尽管不同领域的主要失败模式各不相同，例如，医疗保健中的可解释性、能源电网中的鲁棒性以及地下勘探中的非唯一性，但所有失败都源于一个共同的结构机制。通过将焦点从以模型为中心的评估转向系统级对齐，该框架为评估和管理工业人工智能系统的可靠性提供了原则性基础。
英文摘要
Artificial intelligence is increasingly deployed in critical industrial domains, including healthcare, energy grids, subsurface exploration, where failures can have severe consequences for human safety, system stability, and economic outcomes. Yet AI is still evaluated primarily through benchmark accuracy, a model-centric metric that fails to capture the structural complexity and risks of real-world deployment. We propose a framework that views industrial AI reliability as a problem of structural alignment across four interacting worlds: physical, representational, machine, and human cognitive. These worlds are connected through two interfaces: digitalization, linking physical reality to computational representations, and goal encoding, translating human cognition to the machine objectives. Together, they define the space of admissible solutions. We characterize the solution space through four attributes: existence, non-uniqueness, robustness, and interpretability and show how mismatches arise at interfaces and propagate across worlds to produce reliability failures. Applications to healthcare, energy grids, and subsurface exploration illustrate that although dominant failure modes differ across domains, for example, interpretability in healthcare, robustness in energy grids, and non-uniqueness in subsurface exploration, all originate from a shared structural mechanism. By shifting the focus from model-centric evaluation to system-level alignment, this framework offers a principled foundation for assessing and governing reliability in industrial AI systems.
