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Measuring Development 2024: AI, the Next Generation

May 2, 2024
Washington, DC

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's Development Impact (DIME) department and Development Data Group (DECDG), the Center for Effective Global Action (CEGA), and and the development community at the University of Chicago are excited to explore this topic at our tenth annual Measuring Development (MeasureDev) Conference, “AI, The Next Generation.”

MeasureDev 2024 will feature 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 will feature speakers 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 welcome work-in-progress with limited or pending results. This year, MeasureDev is calling 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
    ○ Using LLMs or AI-generated images like maps for evidence-based policy
    ○ Integrating LLMs or Natural Language Processing in workows 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
    ○ 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!

We will receive applications until February 25th at 11:59 PM PST. Submissions received by Feb 18th will be prioritized by the review committee.

Featured Speakers

The World Bank

Daniel Björkegren

Head of AI and Development initiative at the Center for Development Economics and Policy (CDEP), Columbia University
Daniel Björkegren works on digital transformation and applied machine learning, with a focus on developing economies. He works on methods to make algorithms more humane: robust, transparent, and better aligned with societal values. 

Headshot of Uyi Stewart

Uyi Stewart, Ph.D.

Chief Data and Technology Officer
Uyi Stewart oversees all of’s programmatic initiatives which today include the Generative AI Skills ChallengeInclusive Growth and Recovery Challenge, the Capacity Accelerator Network, and Epiverse. He is a trailblazing expert in data science, artificial intelligence, systems thinking, and strategic digital innovation.

More speakers to be announced.

The World Bank's Development Impact (DIME) department generates high-quality and operationally relevant data and research to transform development policy, help reduce extreme poverty, and secure shared prosperity. 

The Development Data Group (DECDG) is the World Bank's center of excellence on data and statistics, involved in the entire life cycle of data: from its collection, curation, and management through its analysis, visualization, dissemination, as well as enabling its use by policymakers and citizens.

The Center for Effective Global Action (CEGA) is a research hub at the University of California, Berkeley that generates evidence decision-makers use to reduce global poverty.

The development community at the University of Chicago uses the tools of economics to identify, test, refine, and scale innovations with the potential to benefit millions of people.