Apply to be a Speaker

*Applications will be received until January 30, 2026 at 11:59 PM PST.*

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 the Center for Effective Global Action (CEGA) 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 will be received until January 30, 2026 at 11:59 PM PST.*

 

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. 

To be announced

To be announced

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