Incorporating AI into the Inner Circle of Emotional Intelligence for Sustainability
Bibliographic record
Abstract
This paper delves into the fusion of artificial intelligence (AI) and emotional intelligence (EQ) by analyzing the frameworks of international sustainability agendas driven by UNESCO, WEF, and UNICEF. It explores the potential of AI integrated with EQ to effectively address the Sustainable Development Goals (SDGs), with a focus on education, healthcare, and environmental sustainability. The integration of EQ into AI use is pivotal in using AI to improve educational outcomes and health services, as emphasized by UNESCO and UNICEF’s significant initiatives. This paper highlights the evolving role of AI in understanding and managing human emotions, particularly in personalizing education and healthcare. It proposes that the ethical use of AI, combined with EQ principles, has the power to transform societal interactions and decision-making processes, leading to a more inclusive, sustainable, and healthier global community. Furthermore, this paper considers the ethical dimensions of AI deployment, guided by UNESCO’s recommendations on AI ethics, which advocate for transparency, accountability, and inclusivity in AI developments. It also examines the World Economic Forum’s insights into AI’s potential to revolutionize learning and healthcare in underserved populations, emphasizing the significance of fair AI advancements. By integrating perspectives from prominent global organizations, this paper offers a strategic approach to combining AI with EQ, enhancing the capacity of AI systems to meaningfully address global challenges. In conclusion, this paper advocates for the establishment of a new Sustainable Development Goal, SDG 18, focused on the ethical integration of AI and EQ across all sectors, ensuring that technology advances the well-being of humanity and global sustainability.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".