AIxMultilateralism: Why We Need Redlines for Data, with Emily Tucker

AIxMultilateralism: Why We Need Redlines for Data, with Emily Tucker

From The Next Page by United Nations Library & Archives Geneva

April 21, 2026 · 32 min · Episode 181

About this episode

Emily Tucker discusses the need for redlines for data in the context of AI and international cooperation.

This is AI x Multilateralism, a playlist of conversations at the UN Library & Archives Geneva where we’re joined by experts who help us unpack the many ideas and issues at the nexus of AI and international cooperation. For this conversation we’re joined by Emily Tucker, Executive Director at the Center on Privacy & Technology and Adjunct Professor of Law, at Georgetown Law. There are many calls today to enact redlines for AI, but what about redlines for data? In this episode, we explore Emily’s work and research on what’s called datafication. She shares what this means, the impact of datafication on political participation and the public interest, and the implications for our collective capacity to create the futures we want as communities and societies. She also reflects on three priorities for data redlines, and what multilateral fora should be asking when it comes to how data is collected and used in today’s world. Resources: Learn about the Center on Privacy & Technology at Georgetown Law Read Emily's article "To Have Democracy, We Must Contest Data" on TechPolicy.Press Consult Emily's recommendation: "Datafication", by Ulises A Mejias and Nick Couldry (open…

People in this episode

Host: Natalie Alexander Julien

Guest: Emily Tucker

Topics covered

  • AI
  • multilateralism
  • data redlines
  • datafication
  • political participation
  • public interest

Keywords

  • AI
  • datafication
  • multilateralism
  • data redlines
  • political participation
  • public interest

Mentioned in this episode

Organizations: Center on Privacy & Technology, Georgetown Law, The Distributed AI Research Institute

Books & works: To Have Democracy, We Must Contest Data

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