Sustainability and AI: Prioritizing Environmental Considerations in Tech Advancements
Bibliographic record
Abstract
New advancements in artificial intelligence (AI) have positioned this transformative technology at the forefront of innovation, offering unprecedented opportunities to address some of the most pressing challenges of our time. Among these challenges, sustainability stands out as a critical area where AI can make a significant and meaningful impact. The literature emphasizes the imperative for a holistic approach to sustainable AI that encompasses environmental, social, and economic dimensions. This comprehensive perspective is crucial for maximizing the potential benefits of AI while minimizing any adverse impacts on the planet and society. Building public trust in AI through transparent, responsible practices is paramount for ensuring the long-term sustainability and ethical deployment of AI technologies. As we stand at the confluence of technological innovation and environmental stewardship, it is incumbent upon researchers, policymakers, and industry leaders to embrace the principles of sustainable AI. By doing so, we can harness the power of AI to not only drive economic growth and technological advancement but also to safeguard our planet for future generations. The time to act is now, and the path forward requires a concerted effort to integrate sustainability at the core of AI development and application. Through such endeavors, we can achieve a future where technology and nature coexist in harmony, paving the way for a sustainable, inclusive, and prosperous world for all.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".