# A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures

arXiv: https://arxiv.org/abs/2504.16133 · HTML: https://mlsanity.com/paper/2504.16133/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support](https://mlsanity.com/paper/2302.12389/) (2023)
- [Gotta Learn Fast: A New Benchmark for Generalization in RL](https://mlsanity.com/paper/1804.03720/) (2018)
- [Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger](https://mlsanity.com/paper/1712.09344/) (2017)
- [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021)
- [Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World](https://mlsanity.com/paper/2103.16511/) (2021)
- [The five Is: Key principles for interpretable and safe conversational AI](https://mlsanity.com/paper/2108.13766/) (2021)
- [Modeling Scalability of Distributed Machine Learning](https://mlsanity.com/paper/1610.06276/) (2016)

## Built on by (most cited first, 2 of 2)

- [Generation-Time vs. Post-hoc Citation: A Holistic Evaluation of LLM Attribution](https://mlsanity.com/paper/2509.21557/) (2025)
- [Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty](https://mlsanity.com/paper/2508.08992/) (2025)

## Abstract (arXiv)

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.