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An Economic Perspective on Algorithmic Fairness

Author

Listed:
  • Ashesh Rambachan
  • Jon Kleinberg
  • Jens Ludwig
  • Sendhil Mullainathan

Abstract

There are widespread concerns that the growing use of machine learning algorithms in important decisions may reproduce and reinforce existing discrimination against legally protected groups. Most of the attention to date on issues of "algorithmic bias" or "algorithmic fairness" has come from computer scientists and machine learning researchers. We argue that concerns about algorithmic fairness are at least as much about questions of how discrimination manifests itself in data, decision-making under uncertainty, and optimal regulation. To fully answer these questions, an economic framework is necessary—and as a result, economists have much to contribute.

Suggested Citation

  • Ashesh Rambachan & Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan, 2020. "An Economic Perspective on Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 91-95, May.
    • Handle: RePEc:aea:apandp:v:110:y:2020:p:91-95
    DOI: 10.1257/pandp.20201036
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    Citations

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    Cited by:

    1. Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021. "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, vol. 53(52), pages 5989-6001, November.
    2. Jonathan Roth & Guillaume Saint-Jacques & YinYin Yu, 2021. "An Outcome Test of Discrimination for Ranked Lists," Papers 2111.07889, arXiv.org.
    3. Krikamol Muandet, 2022. "Impossibility of Collective Intelligence," Papers 2206.02786, arXiv.org.
    4. Aridor, Guy & Jiménez-Durán, Rafael & Levy, Ro'ee & Song, Lena, 2024. "The Economics of Social Media," CEPR Discussion Papers 18821, C.E.P.R. Discussion Papers.
    5. Janssen, Patrick & Sadowski, Bert M., 2021. "Bias in Algorithms: On the trade-off between accuracy and fairness," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238032, International Telecommunications Society (ITS).
    6. Aridor, Guy & Jiménez-Durán, Rafael & Levy, Ro'ee & Song, Lena, 2024. "The Economics of Social Media," CEPR Discussion Papers 18821, C.E.P.R. Discussion Papers.
    7. Yoan Hermstrüwer & Pascal Langenbach, 2022. "Fair Governance with Humans and Machines," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2022_04, Max Planck Institute for Research on Collective Goods, revised 01 Mar 2023.
    8. Chen, Yutong, 2024. "Does the gig economy discriminate against women? Evidence from physicians in China," Journal of Development Economics, Elsevier, vol. 169(C).
    9. Di Wu, 2024. "The effects of data preprocessing on probability of default model fairness," Papers 2408.15452, arXiv.org.

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

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