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Algorithmic Fairness

Author

Listed:
  • Jon Kleinberg
  • Jens Ludwig
  • Sendhil Mullainathan
  • Ashesh Rambachan
Abstract
Concerns that algorithms may discriminate against certain groups have led to numerous efforts to 'blind' the algorithm to race. We argue that this intuitive perspective is misleading and may do harm. Our primary result is exceedingly simple, yet often overlooked. A preference for fairness should not change the choice of estimator. Equity preferences can change how the estimated prediction function is used (e.g., different threshold for different groups) but the function itself should not change. We show in an empirical example for college admissions that the inclusion of variables such as race can increase both equity and efficiency.

Suggested Citation

  • Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2018. "Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 22-27, May.
    • Handle: RePEc:aea:apandp:v:108:y:2018:p:22-27
    Note: DOI: 10.1257/pandp.20181018
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    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," CEPR Discussion Papers 15418, C.E.P.R. Discussion Papers.
    2. Nils Kobis & Luca Mossink, 2020. "Artificial Intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry," Papers 2005.09980, arXiv.org, revised Sep 2020.
    3. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
    4. Phyllis Asorh Oteng & Victor Curtis Lartey & Amos Kwasi Amofa, 2023. "Modeling the Macroeconomic and Demographic Determinants of Life Insurance Demand in Ghana Using the Elastic Net Algorithm," SAGE Open, , vol. 13(3), pages 21582440231, September.
    5. Christophe Hurlin & Christophe Perignon & Sébastien Saurin, 2021. "The Fairness of Credit Scoring Models," Working Papers hal-03501452, HAL.
    6. Ashesh Rambachan & Jon Kleinberg & Sendhil Mullainathan & Jens Ludwig, 2020. "An Economic Approach to Regulating Algorithms," NBER Working Papers 27111, National Bureau of Economic Research, Inc.
    7. Bauer, Kevin & Pfeuffer, Nicolas & Abdel-Karim, Benjamin M. & Hinz, Oliver & Kosfeld, Michael, 2020. "The terminator of social welfare? The economic consequences of algorithmic discrimination," SAFE Working Paper Series 287, Leibniz Institute for Financial Research SAFE.
    8. Annie Liang & Jay Lu & Xiaosheng Mu & Kyohei Okumura, 2021. "Algorithm Design: A Fairness-Accuracy Frontier," Papers 2112.09975, arXiv.org, revised May 2024.
    9. Laura Blattner & Scott Nelson, 2021. "How Costly is Noise? Data and Disparities in Consumer Credit," Papers 2105.07554, arXiv.org.
    10. Wenlong Sun & Olfa Nasraoui & Patrick Shafto, 2020. "Evolution and impact of bias in human and machine learning algorithm interaction," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-39, August.
    11. Charlson, G., 2022. "Digital Gold? Pricing, Inequality and Participation in Data Markets," Janeway Institute Working Papers 2225, Faculty of Economics, University of Cambridge.
    12. Charlson, G., 2022. "Digital gold? Pricing, inequality and participation in data markets," Cambridge Working Papers in Economics 2258, Faculty of Economics, University of Cambridge.
    13. Jon Kleinberg & Sendhil Mullainathan, 2019. "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability," NBER Working Papers 25854, National Bureau of Economic Research, Inc.
    14. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    15. Anna Zink & Sherri Rose, 2020. "Fair regression for health care spending," Biometrics, The International Biometric Society, vol. 76(3), pages 973-982, September.
    16. Leo Leppänen & Hanna Tuulonen & Stefanie Sirén-Heikel, 2020. "Automated Journalism as a Source of and a Diagnostic Device for Bias in Reporting," Media and Communication, Cogitatio Press, vol. 8(3), pages 39-49.
    17. Chris Lam, 2024. "Debiasing Alternative Data for Credit Underwriting Using Causal Inference," Papers 2410.22382, arXiv.org, revised Oct 2024.
    18. Chen, Yutong, 2024. "Does the gig economy discriminate against women? Evidence from physicians in China," Journal of Development Economics, Elsevier, vol. 169(C).
    19. Claire Lazar Reich, 2021. "Affirmative Action vs. Affirmative Information," Papers 2102.10019, arXiv.org, revised Oct 2024.
    20. Karaenke, Paul & Bichler, Martin & Merting, Soeren & Minner, Stefan, 2020. "Non-monetary coordination mechanisms for time slot allocation in warehouse delivery," European Journal of Operational Research, Elsevier, vol. 286(3), pages 897-907.
    21. Emily Owens & CarlyWill Sloan, 2023. "Can text messages reduce incarceration in rural and vulnerable populations?," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(4), pages 992-1009, September.

    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination

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