Ethical Considerations in Artificial Intelligence (AI) Applications in Smart Grid
Bibliographic record
Abstract
The rapid development of artificial intelligence (AI) tools, particularly advanced systems like large language models and creative AI programs, has raised urgent questions about how we should ethically develop and manage these technologies. As the global adoption of smart grid and digital power infrastructure accelerates, the role of AI becomes increasingly significant in optimizing operations like forecasting, maintenance, and energy distribution. Despite several works in the literature relating to ethical AI in domains like medicine and agriculture, a framework and recommendation are missing from the power grid lens. This study introduces a framework of ethical principles tailored to the smart grid context. Through this framework, we identify key methods and strategies, present case studies, and discuss challenges associated with ethical AI implementation in smart grids. Our research reveals a constant tug-of-war: while AI can revolutionize how grids operate; we can't ignore the risks of prioritizing efficiency over fairness. We also discuss significant challenges and outline future research directions for ethical AI use in power grid systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".