A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures
It addresses safety-critical systems like energy and aeronautics, but the framework appears incremental as it builds on existing interdisciplinary approaches without new empirical results.
The paper tackles the challenge of designing AI-based decision systems for critical infrastructures by proposing a holistic conceptual framework that integrates human and AI capabilities, with a case study on power grid management.
The interaction between humans and AI in safety-critical systems presents a unique set of challenges that remain partially addressed by existing frameworks. These challenges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the necessity for robust and safe decision-making. A framework that holistically integrates human and AI capabilities while addressing these concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective systems. This paper proposes a holistic conceptual framework for critical infrastructures by adopting an interdisciplinary approach. It integrates traditionally distinct fields such as mathematics, decision theory, computer science, philosophy, psychology, and cognitive engineering and draws on specialized engineering domains, particularly energy, mobility, and aeronautics. Its flexibility is further demonstrated through a case study on power grid management.