Artificial Intelligence & Machine Learning

The webinars introduced Digital Public Goods and the Digital Public Goods Standard, exploring their importance in promoting inclusive and equitable digital access.

The report offers a summary of the "AI for All: Bridging Divides, Building a Sustainable Future" conference, which fostered a dialogue on the role of AI in shaping a more equitable world.

This report from the UN and the ILO addresses the critical issue of the uneven adoption of AI and its implications for global equity, fairness, and social justice.

This event explored the transformative role of AI in evaluation practices and its implications.

This research conducted by UN Women and the UN University Institute in Macau (UNU Macau) examines the opportunities and risks of AI from a women, peace and security (WPS) lens in South-East Asia

Contribute to the upcoming General Assembly report of the UN Special Rapporteur on the right to education.

UNDP Accelerator Labs Network and Nesta’s Centre for Collective Intelligence Design launch "UNTAPPED: Collective Intelligence for Climate Action"

Explore how 40 Entities are leveraging new approaches and cutting-edge technologies in their work.

The WFP Innovation Accelerator Year in Review 2023 showcases the tangible impact of its efforts to source, support and scale innovations ending hunger and achieving the SDGs.

WFP's first-ever global data strategy is rooted in the WFP I&T strategy and charts an ambitious course for the organization's use of data and emerging technologies to accelerate progress toward the 2030 UN Sustainable Development Goals.

The Toolkit for Responsible AI Innovation in Law Enforcement supports the global law enforcement community in their journey towards responsible AI innovation.

Witness the newest cohort of cutting-edge startups and social entrepreneurs pitch live in front of potential funders, government officials, and industry leaders.

Leveraging AI, this report examines the technology accelerator trends across publicly available FAO knowledge reports, technical guidance and convening summaries.

This study explores biases in three significant large language models (LLMs): OpenAI’s GPT-2 and ChatGPT, along with Meta’s Llama 2, highlighting their role in both advanced decision-making systems and as user-facing conversational agents. Across multiple studies, the brief reveals how biases emerge in the text generated by LLMs, through gendered word associations, positive or negative regard for gendered subjects, or diversity in text generated by gender and culture.

This paper proposes the use of synthetic training data generated by large language models to improve machine learning SDG classifiers. It shows that supplementing existing training data with synthetic data produced by the ChatGPT tool improves the performance of the SDGClassy classifier.