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Discrimination In The Age Of Algorithms

Author

Listed:
  • Jon Kleinberg
  • Jens Ludwig
  • Sendhil Mullainathan
  • Cass R. Sunstein

Abstract

The law forbids discrimination. But the ambiguity of human decision-making often makes it extraordinarily hard for the legal system to know whether anyone has actually discriminated. To understand how algorithms affect discrimination, we must therefore also understand how they affect the problem of detecting discrimination. By one measure, algorithms are fundamentally opaque, not just cognitively but even mathematically. Yet for the task of proving discrimination, processes involving algorithms can provide crucial forms of transparency that are otherwise unavailable. These benefits do not happen automatically. But with appropriate requirements in place, the use of algorithms will make it possible to more easily examine and interrogate the entire decision process, thereby making it far easier to know whether discrimination has occurred. By forcing a new level of specificity, the use of algorithms also highlights, and makes transparent, central tradeoffs among competing values. Algorithms are not only a threat to be regulated; with the right safeguards in place, they have the potential to be a positive force for equity.

Suggested Citation

  • Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Cass R. Sunstein, 2019. "Discrimination In The Age Of Algorithms," NBER Working Papers 25548, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:25548
    Note: CH DEV ED EH LE LS PE
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    Citations

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    Cited by:

    1. Michael Faure & Shu Li, 0. "Risk shifting in the context of 3D printing: an insurability perspective," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 0, pages 1-26.
    2. Stevenson, Megan T. & Doleac, Jennifer, 2019. "Algorithmic Risk Assessment in the Hands of Humans," IZA Discussion Papers 12853, Institute of Labor Economics (IZA).
    3. Patrick Kline & Christopher Walters, 2019. "Audits as Evidence: Experiments, Ensembles, and Enforcement," Papers 1907.06622, arXiv.org, revised Jul 2019.
    4. S. Mills & S. Costa & C. R. Sunstein, 2023. "AI, Behavioural Science, and Consumer Welfare," Journal of Consumer Policy, Springer, vol. 46(3), pages 387-400, September.
    5. Anne-Marie Nussberger & Lan Luo & L. Elisa Celis & M. J. Crockett, 2022. "Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Ning Li & Huaikang Zhou & Mingze Xu, 2024. "From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management," Papers 2408.05328, arXiv.org.
    7. Michael Faure & Shu Li, 2020. "Risk shifting in the context of 3D printing: an insurability perspective," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(3), pages 482-507, July.

    More about this item

    JEL classification:

    • H0 - Public Economics - - General
    • I0 - Health, Education, and Welfare - - General
    • K0 - Law and Economics - - General

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