Trustworthy artificial intelligence in the energy sector: Landscape analysis and evaluation framework
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 65 references that are in the map. First posted on arXiv 25 Nov 2024.
Builds on
19 of 65 references.
References · 19
- 2016Concrete Problems in AI Safetycited by 1,582
- 2018Datasheets for Datasetscited by 1,371
- 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
- 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
- 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
- 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
- 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
- 2018Adversarial Robustness Toolbox v1.0.0cited by 214
- 2021Trustworthy AI: From Principles to Practicescited by 206
- 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
- 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
- 2017Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigationcited by 24
7 more
- 2023A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy driverscited by 6
- 2023In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performancecited by 5
- 2023Targeted demand response for flexible energy communities using clustering techniquescited by 5
- 2020SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detectioncited by 4
- 2020Avoiding Occupancy Detection from Smart Meter using Adversarial Machine Learningcited by 4
- 2023Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time seriescited by 4
- 2023DeepTSF: Codeless machine learning operations for time series forecastingcited by 3
Built on by
Citing papers per year: 2025: 2.
2 papers.