A Lifecycle Approach for Artificial Intelligence Ethics in Energy Systems

Nicole El-Haber, Donna Burnett, Alison Halford, Kathryn Stamp, Daswin De Silva (), Milos Manic and Andrew Jennings
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Nicole El-Haber: Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Donna Burnett: Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Alison Halford: Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 5FB, UK
Kathryn Stamp: Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 5FB, UK
Daswin De Silva: Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Milos Manic: Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
Andrew Jennings: Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia

Energies, 2024, vol. 17, issue 14, 1-11

Abstract: Despite the increasing prevalence of artificial intelligence (AI) ethics frameworks, the practical application of these frameworks in industrial settings remains limited. This limitation is further augmented in energy systems by the complexity of systems composition and systems operation for energy generation, distribution, and supply. The primary reason for this limitation is the gap between the conceptual notion of ethics principles and the technical performance of AI applications in energy systems. For instance, trust is featured prominently in ethics frameworks but pertains to limited relevance for the robust operation of a smart grid. In this paper, we propose a lifecycle approach for AI ethics that aims to address this gap. The proposed approach consists of four phases: design, development, operation, and evaluation. All four phases are supported by a central AI ethics repository that gathers and integrates the primary and secondary dimensions of ethical practice, including reliability, safety, and trustworthiness, from design through to evaluation. This lifecycle approach is closely aligned with the operational lifecycle of energy systems, from design and production through to use, maintenance, repair, and overhaul, followed by shutdown, recycling, and replacement. Across these lifecycle stages, an energy system engages with numerous human stakeholders, directly with designers, engineers, users, trainers, operators, and maintenance technicians, as well as indirectly with managers, owners, policymakers, and community groups. This lifecycle approach is empirically evaluated in the complex energy system of a multi-campus tertiary education institution where the alignment between ethics and technical performance, as well as the human-centric application of AI, are demonstrated.

Keywords: AI ethics; responsible AI; energy AI; AI risks; AI lifecycle; energy systems; implementation science (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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