ML for Economic Policy Workshop

NEURIPS 2020

December 11, 2020

12:00 PM - 5:45 PM

Machine learning offers enormous potential to transform our understanding of economics, economic decision making, and public policy. Yet its adoption by economists, social scientists, and policymakers remains nascent.

This workshop will highlight both the opportunities as well as the barriers to the adoption of ML in economics. In particular, we aim to accelerate the use of machine learning to rapidly develop, test, and deploy effective economic policies that are grounded in representative data.

This workshop will expose some of the critical socio-economic issues that stand to benefit from applying machine learning, expose underexplored economic datasets and simulations, and identify machine learning research directions that would have a significant positive socio-economic impact. This includes policies and mechanisms that target socio-economic issues such as diversity and fair representation in economic outcomes, economic equality, and improving economic opportunity.

KEYNOTE SPEAKERS

  • Micheal Kearns (UPenn)
  • Susan Athey (Stanford)
  • Sendhil Mullainathan (University of Chicago)
  • Doina Precup (Deepmind, McGill)

 

PANELISTS

  • Sharad Goel (Stanford)
  • Daniel Bjorkegren (Brown)
  • Eva Tardos (Cornell)
  • Rediet Abebe (Harvard)
  • Thore Graepel (Deepmind, UCL)
  • Doyne Farmer (Oxford)
  • Marietje Schaake (Stanford)
  • Emma Pierson (Cornell)

 

More information

caret-arrow