The Role of Artificial Intelligence in Achieving the United Nations Sustainable Development Goals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it