2024/11/25 by Sotiris Pelekis, Pelekis, Sotiris, Evangelos Karakolis +11 · 1 citation
Engineering · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2412.07782
openalex publication_date 2024/11/25 · openalex created_date 2024/12/13 · openalex updated_date 2026/07/28
The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAI) within the European Union (EU), with a specific focus on the energy sector. The analysis encompasses legal frameworks, directives, initiatives, and standards like the AI Ethics Guidelines for Trustworthy AI (EGTAI), the Assessment List for Trustworthy AI (ALTAI), the AI act, and relevant CEN-CENELEC standardization efforts, as well as EU-funded projects such as AI4EU and SHERPA. Subsequently, we introduce a new TAI application framework, called E-TAI, tailored for energy applications, including smart grid and smart building systems. This framework draws inspiration from EGTAI but is customized for AI systems in the energy domain. It is designed for stakeholders in electrical power and energy systems (EPES), including researchers, developers, and energy experts linked to transmission system operators, distribution system operators, utilities, and aggregators. These stakeholders can utilize E-TAI to develop and evaluate AI services for the energy sector with a focus on ensuring trustworthiness throughout their development and iterative assessment processes.