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AI in Critical Infrastructures – Explainability and Models
Citation Link: https://doi.org/10.15480/882.13498
Publikationstyp
Conference Paper
Date Issued
2024-09-18
Sprache
English
Author(s)
Technical University of Darmstadt
TORE-DOI
Start Page
66
End Page
73
Citation
35. Forum Bauinformatik, fbi 2024: 66-73
Contribution to Conference
Publisher
Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen
Peer Reviewed
true
Critical infrastructures such as power grids, transportation networks, and water systems are essential to national economies and societal well-being. Integrating Artificial Intelligence (AI) into these systems could enhance productivity and operational resilience. However, the adoption of AI in critical infrastructures necessitates a focus on explainability to ensure transparency, trust, and regulatory compliance. This paper explores inherently explainable models (IEMs) and post hoc explainable models (PHEMs) within the domain of critical infrastructures. By examining regulatory requirements, analyzing different AI models designed for explainability, and comparing these models, this paper provides a comprehensive overview of strategies for selecting AI systems that enhance transparency and compliance. The findings underscore the importance of choosing appropriate AI models to ensure safe, reliable, and legally accountable AI implementation in critical infrastructure, ultimately supporting societal functions and public safety.
Subjects
Critical Infrastructure
Explainable Artificial Intelligence
Interpretability
Resilience
Transparency
DDC Class
006: Special computer methods
333.7: Natural Resources, Energy and Environment
681: Precision Instruments and Other Devices
Publication version
publishedVersion
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Name
AI in Critical Infrastructure -– Explainability and Models.pdf
Type
Main Article
Size
165.56 KB
Format
Adobe PDF