# Being Accountable is Smart: Navigating the Technical and Regulatory Landscape of AI-based Services for Power Grid

arXiv: https://arxiv.org/abs/2408.01121 · HTML: https://mlsanity.com/paper/2408.01121/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 59 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning](https://mlsanity.com/paper/2202.05338/) (2022)
- [Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigation](https://mlsanity.com/paper/1710.06921/) (2017)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [The Conflict Between Explainable and Accountable Decision-Making Algorithms](https://mlsanity.com/paper/2205.05306/) (2022)
- [Towards Accountability in the Use of Artificial Intelligence for Public Administrations](https://mlsanity.com/paper/2105.01434/) (2021)
- [Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies](https://mlsanity.com/paper/2004.12929/) (2020)
- [Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning](https://mlsanity.com/paper/2305.08885/) (2023)
- [Designing Accountable Systems](https://mlsanity.com/paper/2101.08102/) (2021)
- [Responsible Design Patterns for Machine Learning Pipelines](https://mlsanity.com/paper/2306.01788/) (2023)
- [Foreseeing the Impact of the Proposed AI Act on the Sustainability and Safety of Critical Infrastructures](https://mlsanity.com/paper/2208.14451/) (2022)

## Abstract (arXiv)

The emergence of artificial intelligence and digitization of the power grid introduced numerous effective application scenarios for AI-based services for the smart grid. Nevertheless, adopting AI in critical infrastructures presents challenges due to unclear regulations and lacking risk quantification techniques. Regulated and accountable approaches for integrating AI-based services into the smart grid could accelerate the adoption of innovative methods in daily practices and address society's general safety concerns. This paper contributes to this objective by defining accountability and highlighting its importance for AI-based services in the energy sector. It underlines the current shortcomings of the AI Act and proposes an approach to address these issues in a potential delegated act. The proposed technical approach for developing and operating accountable AI-based smart grid services allows for assessing different service life cycle phases and identifying related accountability risks.