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*Call for Speakers is now closed*

Over the past decade, major advancements in transparency and reproducibility, from data and code sharing to pre-analysis plans and replication efforts, have reshaped empirical research. But the field is now at a turning point: AI and increasingly complex computational methods are transforming how we analyze ever-larger, often confidential datasets. These advances promise more timely and granular insights, but also raise new questions:  How can we ensure research remains trustworthy and reproducible? How do we balance openness with privacy and security? And how do we keep up with the pace of technological change?

The World Bank’s Development Impact Group (DECDI), Development Data Group (DECDG), and Data Academy, the Center for Effective Global Action (CEGA), and the University of Chicago’s Becker Friedman Institute for Economics are excited to explore these questions at the 12th annual Measuring Development (MeasureDev) Conference, “Open Science in the Age of AI: Balancing Privacy and Transparency.”

MeasureDev 2026 will bring together policymakers, researchers, and practitioners who are shaping the future of transparent, credible, and privacy-aware evidence generation. The event will reflect on a decade of progress in open science, spanning data and code sharing, pre-analysis plans, replication initiatives, and more, while charting a path forward for transparency and replicability in research in light of rapid changes in technology, data access, and computational methods.

*Applications are now closed*

 

The MeasureDev planning committee welcomes early-stage ideas, works-in-progress, and completed research. In addition to empirical papers, we invite submissions related to the tools, platforms, and practices that are advancing transparency and reproducibility in development economics and adjacent fields. Submissions may include, but are not limited to:

 

Transparency in the age of AI: 

  • Verification and accountability of predictive models and generative technologies 
  • Addressing bias, provenance, and explainability within applied analyses 
  • Institutional or technical barriers to transparency and reproducibility in AI models 

Adapting standards and AI tools to scale research transparency and reproducibility: 

  • Assessments of the trade-offs between data privacy and replicability 
  • Methods to ensure data integrity when relying on sensitive and confidential data 
  • Influence of aggregated or synthetic data on reproducibility 
  • Adapting accessibility and replicability standards for computationally intensive research  

How open-science practices are transforming AI-informed research, data, and policy: 

  • Frameworks and tools to advance open science and data
  • How transparency and reproducibility standards affects trust and evidence adoption among policymakers 
  • Evidence on the impacts of open science policies on methods, citations, and reuse 
  • Analysis of how transparency influences perceptions and redistributes costs and benefits among researchers. 

 

8:30 AM

 

 Welcome to Measuring Development 2026 | Registration & Networking
9:00 AM Opening remarks | Arianna Legovini (World Bank), Sean Luna McAdams (CEGA), Benjamin Krause (UChicago)
9:20 AM

Adapting Standards and AI Tools to Scale Research Transparency and Reproducibility

Chair: Abel Brodeur, University of Ottawa, Institute for Replication
 
  • Jonas Weinert (World Bank, London School of Economics) | Between Efficiency, Recall, and Transparency: An Evaluation of Guided Literature Screening Using LLMs - MetaScreener
  • Lars Vilhuber (Cornell University) | TRACE: Trusting Computational Research Without Repeating it
  • Bruno Barbarioli (Institute for Replication) | The AI Replication Engine: Autonomous Verification of Empirical Research in the Social Sciences
  • Aubrey Jolex (Innovations for Poverty Action) | Code in the Machine: Can Large Language Models Reproduce Econometric Analyses?
10:25 AM Coffee Break
10:45 AM AI Tools for Evidence Synthesis
 
  • Devika Lakhote (3ie) | Dev chat
  • Linxi Wang (World Bank) | Impact AI
  • Janine Aguilera Mesa (Independent Researcher) | The Global Development Portfolio Atlas: From Fragmented Evidence to Portfolio Intelligence
  • Zezhen Wu (Agency Fund) | Generative AI Evaluation in the Development Sector: A Living Playbook
11:50 AM Keynote Speaker | Ted Miguel (University of California - Berkeley)
12:40 PM Lunch
1:40 PM Panel | Operationalizing Open Science in the Age of AI: Changing Norms, Standards, and Practices through Institutional Leadership
 
  • Arianna Legovini, Director of Development Impact Group, World Bank
  • Haishan Fu, Chief Statistician and Director of the Development Data Group, World Bank
  • Markus Goldstein, Vice President and Senior Fellow, Center for Global Development
2:10 PM

Balancing Openness and Privacy

Chair: Lars Vilhuber, Executive Director, Labor Dynamics Institute, Cornell University | Data Editor, American Economic Association
 
  • Nitin Kohli (University of California - Berkeley) | Enabling Humanitarian Applications with Targeted Differential Privacy
  • Kaitlyn Webb (Pennsylvania State University) | Beyond Anonymization: Formal Privacy-Preserving Data Tools for Replicable Randomized Control Trials
3:15 PM Coffee Break
3:35 PM

Transparency in the Age of AI

Chair: Julia Lane, Professor Emerita, NYU Wagner Graduate School of Public Service | Founder, Coleridge Initiative
 
  • Joao Pedro Azevedo (UNICEF) | AI Reliability for Official Statistics: Benchmarking Large Language Models with the UNICEF Data Warehouse
  • Virginia Ziulu (World Bank) | Beyond LLMs: Transparency, Ethics, and Interpretability in Geospatial Artificial Intelligence
  • Silvia Arini (Statistics Indonesia) | Economics of Open Science: A Repository-Based Framework for Measuring the Intelligence Value of AI and Code Flows
4:40 PM Lightning Talks
 
  • Patrick Adeyemi Ilori (Uppsala University) | Privacy-Preserving Open Science: Evaluating Hybrid Differential Privacy and Synthetic Data Methods for Equitable Development Research
  • Mohammed Ba-Aoum (Blue Cross Blue Shield, National Institutes of Health) | Explainability Across the AI Lifecycle: Engineering Trust, Reproducibility, and Accountability in Development Measurement
  • Oumaima Makhlouk (World Bank) | Confidential-by-Default, Auditable-by-Design: An Evidence-First AI Agent for Consumer-Protection Contract Review

 

5:00 PM

 

Closing & Acknowledgements | Sean Luna McAdams (CEGA) & Maria Ruth Jones (World Bank)

 

5:05 PM

 

Reception

To be announced

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