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This chapter gives an overview of the results of a multi-stakeholder policy workshop held in Estonia in December 2024. The participants were experts from the National Statistical Board, Research Council, Social Insurance Board, Foresight Centre of the Estonian Parliament, Health Board, Tartu City Government, and Centre of IT Impact Studies (University of Tartu). The workshop was inspired by the Estonian case study of an AI-based decision-support system applied in the Estonian Unemployment Insurance Fund which illustrates the complexity of integrating AI into public welfare systems. While Estonia’s digital infrastructure provides a strong foundation, the success of AI-driven decision-making depends on robust data ecosystems and interoperable standards, ethical and participatory governance models, ongoing education, and stakeholder engagement. Future research should explore longitudinal impacts of AI tools on welfare outcomes, develop standardized frameworks for bias assessment, and expand participatory design practices. Estonia’s experience offers valuable lessons for other countries navigating the intersection of data, AI, and social policy.

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Bridging Data and Policy: Disseminating Scientific Insights in Estonia’s AI-Driven Welfare Governance

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Avo Trumm,
  • Maris Männiste,
  • Triin Vihalemm

摘要

This chapter gives an overview of the results of a multi-stakeholder policy workshop held in Estonia in December 2024. The participants were experts from the National Statistical Board, Research Council, Social Insurance Board, Foresight Centre of the Estonian Parliament, Health Board, Tartu City Government, and Centre of IT Impact Studies (University of Tartu). The workshop was inspired by the Estonian case study of an AI-based decision-support system applied in the Estonian Unemployment Insurance Fund which illustrates the complexity of integrating AI into public welfare systems. While Estonia’s digital infrastructure provides a strong foundation, the success of AI-driven decision-making depends on robust data ecosystems and interoperable standards, ethical and participatory governance models, ongoing education, and stakeholder engagement. Future research should explore longitudinal impacts of AI tools on welfare outcomes, develop standardized frameworks for bias assessment, and expand participatory design practices. Estonia’s experience offers valuable lessons for other countries navigating the intersection of data, AI, and social policy.