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  4. Human-AI interaction in safety-critical network infrastructures
 
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September 2025
Journal Article
Title

Human-AI interaction in safety-critical network infrastructures

Abstract
Artificial Intelligence (AI) is transforming every aspect of modern society. It demonstrates a high potential to contribute to more flexible operations of safety-critical network infrastructures under deep transformation to tackle global challenges, such as climate change, energy transition, efficiency, and digital transformation, including increasing infrastructure resilience to natural and human-made hazards. The widespread adoption of AI creates the conditions for a new and inevitable interaction between humans and AI-based decision systems. In such a scenario, creating an ecosystem in which humans and AI interact healthily, where the roles and positions of both actors are well-defined, is a critical challenge for research and industry in the coming years. This perspective article outlines the challenges and requirements for effective human-AI interaction by taking an interdisciplinary point of view that merges computer science, decision-making sciences, psychological constructs, and industrial practices. The work focuses on three emblematic safety-critical scenarios from two different domains: energy (power grids) and mobility (railway networks and air traffic management).
Author(s)
Mussi, Marco
Metelli, Alberto Maria
Restelli, Marcello
Losapio, Gianvito
Bessa, Ricardo J.
Boos, Daniel
Borst, Clark
Leto, Giulia
Castagna, Alberto
Chavarriaga, Ricardo
Dias, Duarte
Egli, Adrian
Eisenegger, Andrina
El Manyari, Yassine
Fuxjäger, Anton
Geraldes, Joaquim
Hamouche, Samira
Hassouna, Mohamed
Fraunhofer-Institut für Energiewirtschaft und Energiesystemtechnik IEE  
Lemetayer, Bruno
Leyli-Abadi, Milad
Liessner, Roman
Lundberg, Jonas
Marot, Antoine
Meddeb, Maroua
Schiaffonati, Viola
Schneider, Manuel
Stadelmann, Thilo
Usher, Julia
Hoof, Herke van
Viebahn, Jan
Waefler, Toni
Zanotti, Giacomo
Journal
iScience  
Project(s)
AI for REAL-world NETwork operation  
Funder
European Commission
Open Access
DOI
10.1016/j.isci.2025.113400
Additional link
Full text
Language
English
Fraunhofer-Institut für Energiewirtschaft und Energiesystemtechnik IEE  
Keyword(s)
  • Human-AI interaction

  • Reinforcement Learning

  • Power Grid Control

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