The Role of Artificial Intelligence in Advancing Energy Justice and Equity
Bibliographic record
Abstract
This chapter closely examines the integration of artificial intelligence (AI) in the energy sector, with a focus on its role in promoting justice and equity. It starts by exploring the impact of AI on the energy transition, highlighting opportunities and challenges in revolutionizing planning, operation, control, and maintenance. The chapter investigates the role of AI in the advancement of energy justice by evaluating AI design through the lens of distributive, procedural, and recognition justice. It emphasizes the necessity of balancing benefits and burdens, ensuring transparency and participation, and meeting the diverse needs of stakeholders. By highlighting the importance of interpretability in transparent decision-making, the discussion explores biases, mitigation strategies, and the imperative for equitable AI algorithms. In addressing broader ethical considerations, the chapter navigates the nuanced balance between privacy, safety, and transparency, underscoring the need for robust frameworks to safeguard individual rights while maintaining efficiency. A practical demonstration of principles discussed in this chapter is provided through a case study on justice-oriented emergency load shedding using reinforcement learning. In conclusion, the chapter emphasizes the importance of energy policy development in guaranteeing justice-oriented approaches and proposes a future path to align AI advances with justice and equity goals.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".