MeasureDev 2026: Open Science in the Age of AI: Balancing Privacy and Transparency

Over the past decade, social scientists and technologists have made significant advancements in transparency and reproducibility: from data and code sharing to pre-analysis plans and replication initiatives. But the field is now at a turning point: artificial intelligence and increasingly complex computational methods are transforming how we analyze data, as researchers face ever-larger and often confidential datasets. Richer data promise to bring 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 12th annual Measuring Development (MeasureDev) Conference, “Open Science in the Age of AI: Balancing Privacy and Transparency” will convene leading researchers, policymakers, and practitioners to review progress over the past decade and chart a path forward for transparency and replicability in light of rapid changes in technology, data access, and computational methods. Co-hosted by the World Bank and the Center for Effective Global Action, MeasureDev will bring together experts who are shaping how credible evidence should be generated for development policy.

Call for Papers

The MeasureDev planning committee invites early-stage works-in-progress and completed papers. In addition to research papers, we welcome submissions related to new tools and technical platforms advancing transparency and reproducibility in development economics and related 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