Summary
Artificial intelligence is being used in important areas like healthcare and energy, where mistakes can be very serious. The authors explain that judging AI only by accuracy misses problems caused by how the AI relates to the real world, the data it uses, and human intentions. They propose a framework that looks at AI reliability as the alignment of four parts: the physical world, data representations, AI models, and human goals. Problems happen when these parts don’t fit well together, and this can cause failures differently in various industries. This new way helps design and evaluate AI systems that work safely and reliably in real-world industrial settings.
What this means in practice
- •For healthcare ai teams: Improve the clarity of AI decisions to meet safety and transparency standards in medical diagnostics and treatment recommendations.
- •For energy grid operators: Enhance the robustness of AI models to maintain stable and safe operation of power systems under changing conditions.
A position paper. It proposes an approach and reports no results.
Authors
Lizhi Xiao, Sihong Wu, Victoria Xiao, Yiqiao Song, Chen Gu, Jianwei Ma, Xinming Wu, Aimé Fournier
Abstract
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.