A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures
Milad Leyli-Abadi (IRT SystemX)
Ricardo J. Bessa (Institute for Systems and Computer Engineering, Technology and Science (INESC TEC))
Jan Viebahn (TenneT TSO B.V.)
Daniel Boos (SBB)
Clark Borst (TU Delft - Aerospace Engineering)
Alberto Castagna (enliteAI)
Ricardo Chavarriaga (Zurich University of Applied Science (ZHAW))
Mohamed Hassouna (Fraunhofer Institute for Energy Economics and Energy System Technology IEE)
Giulia Leto (TU Delft - Aerospace Engineering)
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Abstract
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.