Measuring Development 2024: AI, The Next Generation

Foundational models like large language models (LLMs) have recently commanded widespread public attention—and caution—given their transformational potential for both our economy and society. Naturally, questions loom about how these AI innovations will impact the global development research and policy landscape. If used properly by the right actors, these tools might unlock enormous troves of data and create new opportunities to improve lives around the world.

The World Bank, the Center for Effective Global Action (CEGA), and the University of Chicago Center for the Economics of Innovation and Development (CEID) were excited to explore this topic at our tenth annual Measuring Development (MeasureDev) Conference, “AI, The Next Generation.” The conference featured presentations on AI that span the measurement ecosystem: from efforts to improve and expand responsible data infrastructure in low- and middle-income countries (LMICs) and facilitate the development of a new generation of AI tools, to analysis tailoring foundational models to optimize generative AI (GenAI) including LLMs for social impact. The event featured policymakers from different contexts who are shaping the way these new tools will be adopted and regulated.

Call for Speakers

Given the rapidly evolving nature of this research ecosystem and its implications for public policy, we welcomed work-in-progress with limited or pending results. This year, MeasureDev called for presentations on research, data infrastructure, and governance including:

• Basic and applied research on Generative AI, foundational models, and text-based machine learning including but not limited to:
  • Improving foundational components of LLMs for applications in development
  • Using LLMs or AI-generated images like maps for evidence-based policy
  • Integrating LLMs or Natural Language Processing in workflows to build development data products

• Efforts to build responsible, privacy-preserving, integrated, and interoperable data infrastructure to enable novel AI development like:
  • Improving discoverability and responsible use of development training data
  • Enhancing metadata using NLP
  • Generating and sharing synthetic training data
  • Developing standards in data and script documentation to enhance transparency and explainability of training data and ML models

• Interactive panels on emerging governance models, ethics, and best practices integrating GenAI into decision-making processes, for example: 
  • Approaches to mitigate potential harms like misinformation, bias, privacy violations, and environmental risks
  • Pathways for more open, ethical, and inclusive of AI technologies
  • Challenges for open and reproducible research when using AI technologies

Are you interested in being a speaker? Apply here! Mark your calendars for May 2nd, our registration form for general attendance will be posted in March.