Recordings of the talks at this conference are now available on our Youtube channel.
Links to slides and papers can be found below.

Conference announcement

Questions regarding the fairness of algorithmic decision-making have received much attention in recent years, by both the wider public and in academic debates. In this workshop, motivated by the arguments discussed in Fairness, Equality, and Power, we propose to shift the focus of these debates toward the causal impact of machine learning, AI, and algorithmic decision-making on economic and social inequality, both across and within groups. This workshop aims to bring together participants from several fields, including economics, computer science, statistics, law, sociology, and social policy. Talks will cover theoretical and empirical aspects, and both normative and positive questions.

Possible topics for this conference include, but are not restricted to:

  • Theories of justice and social choice theory, concepts of fairness and discrimination.
  • Learning theory, supervised learning, and targeted treatment assignment.
  • The impact of algorithmic, individualized treatment in pricing, hiring, promotion, and credit scoring on economic inequality.
  • Social welfare analysis and optimal policy theory.
  • The labor market impact of new technologies, automation, and gig work.
  • The political economy of surveillance, data collection, and ownership.
  • Algorithmic management and labor law.

Zoom webinar registration
Registration is required to attend the talks!


  • Date: April 19-20, 2021
  • Location: Zoom.
  • Presentations: Target length 40 minutes, plus 10 minutes of discussion time.
  • Contact: For logistical questions, please contact
  • Papers and Slides: Please also email these to, and we will post them on this website before the conference.
  • Videos of talks will be recorded and posted online; please let us know if you don’t want to be recorded.
  • Talks will be live-streamed, and recordings made available, on this Youtube channel

Confirmed speakers

  • Rediet Abebe (Computer Science, UC Berkeley)
  • Jeremy Adams-Prassl (Law, University of Oxford)
  • Abigail Adams-Prassl (Economics, University of Oxford)
  • Reuben Binns (Computer Science, University of Oxford)
  • Moustapha Cisse (Computer Science, Google AI Accra)
  • Paul Goldsmith-Pinkham (Economics, Yale SOM)
  • Abigail Jacobs (School of Information, University of Michigan)
  • Pauline Kim (Law, Washington University in St. Louis)
  • Joshua Loftus (Statistics, London School of Economics)
  • Salome Viljoen (Law, NYU)


To facilitate participation across time zones, talks will take place over the course of two afternoons, between 3pm and 8pm UK time.

Monday, April 19

3:00pm Maximilian Kasy (Economics)
Opening remarks

3:10pm Abigail Adams-Prassl (Economics)
Artificial Intelligence & Labour Economics: Methods & Substantive Questions

4:00pm Reuben Binns (Computer Science)
Egalitarian justice and machine learning

4:50pm Break

5:10pm Rediet Abebe (Computer Science)
Modeling the Impact of Shocks on Poverty

6:00pm Joshua Loftus (Statistics)
Causality and the normative dimensions of machine learning
Paper 1, Paper 2

6:50pm Break

7:00pm Paul Goldsmith-Pinkham (Economics)
Economics, Discrimination and Inequality
Paper 1, Paper 2

7:50pm Open discussion

Tuesday, April 20

3:00pm Pauline Kim (Law)
Bias in ML Models: Legal Challenges
Paper 1, Paper 2

3:50pm Jeremy Adams-Prassl (Law)
What if Your Boss Was an Algorithm? The Rise of Artificial Intelligence at Work

4:40pm Break

5:00pm Salome Viljoen (Law)
Confronting inequality in the theory of data law

5:50pm Abigail Jacobs (School of Information)
Measurement as governance

6:40pm Break

6:50pm Moustapha Cisse (Computer Science)

7:40pm Open discussion and conclusion