The Role of Artificial Intelligence in Achieving the United Nations Sustainable Development Goals
Bibliographic record
Abstract
The United Nations' 2030 Agenda for Sustainable Development aims to tackle poverty, inequality, and environmental degradation and foster economic growth. This study investigates the transformative potential of artificial intelligence (AI) in achieving these Sustainable Development Goals (SDGs). Analyzing data from 44 sources, the research highlights AI's capacity to address critical challenges in healthcare, education, environmental management, economic growth, and gender equality. AI applications in renewable energy, waste management, disease detection, personalized education, and gender equality are examined. The study also emphasizes the ethical issues associated with AI, such as algorithmic bias, data privacy breaches, and job displacement. To fully leverage AI's potential, it is essential to develop intelligent automation governance systems, foster interdisciplinary research combining AI and sustainability, and promote public-private partnerships. Additionally, enhancing public AI literacy and implementing eco-friendly AI policies are crucial. The study advocates for a holistic ethical framework to maximize AI's benefits while mitigating risks, promoting cross-disciplinary collaboration, and establishing ethical AI standards. By doing so, AI can significantly contribute to a more inclusive, equitable, and sustainable future.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".