PREreview of AI for United Nations Sustainable Development Goals

Review by Mehdi Zoghaib of : “AI for United Nations Sustainable Development Goals”

Source: Mirza Abdul Aleem Baig and al, (2024), AI for United Nations Sustainable Development Goals”, Preprint.

Key words: AI, United Nations, Sustainable Development Goals

Abstract

Artificial Intelligence (AI) has transformative potential to accelerate progress toward achieving the United Nations' Sustainable Development Goals (SDGs). By addressing challenges in healthcare, education, environmental sustainability, economic growth, and gender equality, AI can be a cornerstone of sustainable development. However, ethical, environmental, and societal risks associated with AI deployment require robust governance and interdisciplinary collaboration.

1. Introduction and Context - UN SDGs Framework:

Established in 2015 with 17 interrelated goals addressing global issues like poverty, inequality, and climate change. - AI's integration into SDGs offers solutions across critical areas like healthcare, education, environmental monitoring, and economic development. Examples include predictive analytics in healthcare, adaptive learning in education, and data-driven policy tools for governance. - Ethical challenges (algorithmic bias, data privacy, job displacement) require balanced approaches.

2. AI-Driven Solutions for SDGs - Health and Well-being (SDG 03): - AI aids in early disease detection, personalized treatments, telemedicine, and pandemic resource allocation. - Enhances healthcare access in underserved regions through virtual consultations and remote monitoring.

Education and Gender Equality (SDG 04 & 05): - AI-driven adaptive learning platforms improve education accessibility and quality. - Language translation technologies bridge gaps for diverse populations. - AI promotes gender equality by identifying and mitigating biases in educational resources and career guidance.

Economic Growth and Decent Work (SDG 08 & 09): - Automation, predictive analytics, and innovative technologies drive productivity and create safer workplaces. - Encourages sustainable industrial practices and infrastructure development.

Environmental Conservation (SDG 13, 14, & 15): - AI supports climate modeling, renewable energy optimization, and biodiversity protection. - Promotes circular economy practices, such as efficient waste management and resource conservation.

3. Challenges and Ethical Considerations - Environmental Impact:

AI's high energy demands and carbon footprint pose risks to climate goals. - Lifecycle impacts (e.g., electronic waste, raw material sourcing) necessitate sustainable practices.

Social Equity and Inclusion: - Algorithmic bias risks perpetuating inequalities. - Workforce displacement from automation requires reskilling initiatives.

- Ethical Governance: - Transparent, accountable, and equitable frameworks are essential for managing AI's societal impact.

4. Policy Recommendations

Establish robust AI governance emphasizing accountability, fairness, and transparency, including energy-efficient models and circular economy initiatives.

5. Conclusion

AI's integration into SDGs holds significant promise but demands a holistic and ethical approach. Collaboration among stakeholders, interdisciplinary research, and transparent governance are critical. Emphasizing sustainability, social equity, and ethical principles is key to ensuring AI accelerates SDG progress without exacerbating existing challenges.

Competing interests

The author declares that they have no competing interests.