IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2102.10019.html

Affirmative Action vs. Affirmative Information

Author

Listed:
  • Claire Lazar Reich

Abstract

Critical decisions in hiring, college admissions, and credit lending are guided by predictions made in the presence of uncertainty. While uncertainty imparts errors across all demographic groups, this paper shows that the types of errors vary systematically: Groups with higher average outcomes are typically assigned higher false positive rates, while those with lower average outcomes are assigned higher false negative rates. We characterize the conditions that give rise to this disparate impact and explain why the intuitive remedy to omit demographic variables from datasets does not correct it. Instead of data omission, this paper examines how data acquisition can broaden access to opportunity. The strategy, which we call "Affirmative Information," could stand as an alternative to Affirmative Action.

Suggested Citation

  • Claire Lazar Reich, 2021. "Affirmative Action vs. Affirmative Information," Papers 2102.10019, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:2102.10019
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2102.10019
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Jon Kleinberg & Sendhil Mullainathan, 2019. "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability," NBER Working Papers 25854, National Bureau of Economic Research, Inc.
    3. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2018. "Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 22-27, May.
    4. Vojtěch Bartoš & Michal Bauer & Julie Chytilová & Filip Matějka, 2016. "Attention Discrimination: Theory and Field Experiments with Monitoring Information Acquisition," American Economic Review, American Economic Association, vol. 106(6), pages 1437-1475, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness," Papers 2010.08463, arXiv.org, revised Nov 2025.
    2. Laura Blattner & Scott Nelson, 2021. "How Costly is Noise? Data and Disparities in Consumer Credit," Papers 2105.07554, arXiv.org.
    3. 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.
    4. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    5. Francesco Capozza & Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2021. "Studying Information Acquisition in the Field: A Practical Guide and Review," CEBI working paper series 21-15, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    6. Henning Hermes & Philipp Lergetporer & Fabian Mierisch & Frauke Peter & Simon Wiederhold, 2023. "Discrimination on the Child Care Market: A Nationwide Field Experiment," Working Papers 225, Bavarian Graduate Program in Economics (BGPE).
    7. Katherine B. Coffman & Scott Kostyshak & Perihan O. Saygin & Katie Coffman, 2024. "Choosing and Using Information in Evaluation Decisions," CESifo Working Paper Series 11024, CESifo.
    8. 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.
    9. Abel, Martin & Robbett, Andrea & Stone, Daniel F., 2024. "Partisan Discrimination in Hiring," IZA Discussion Papers 17540, Institute of Labor Economics (IZA).
    10. Alain Cohn & Michel André Maréchal & Frédéric Schneider & Roberto A Weber, 2021. "Frequent Job Changes can Signal Poor Work Attitude and Reduce Employability," Journal of the European Economic Association, European Economic Association, vol. 19(1), pages 475-508.
    11. Daniel Martin & Philip Marx, 2022. "A Robust Test of Prejudice for Discrimination Experiments," Management Science, INFORMS, vol. 68(6), pages 4527-4536, June.
    12. Samuele Centorrino & Jean-Pierre Florens & Jean-Michel Loubes, 2022. "Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables," Papers 2202.08977, arXiv.org.
    13. Chareyron, Sylvain & L'Horty, Yannick & Mbaye, Souleymane & Petit, Pascale, 2023. "Reducing ethnic discrimination through formal warning: Evidence from two combined field experiments," Regional Science and Urban Economics, Elsevier, vol. 98(C).
    14. 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.
    15. Stefan C. Wolter & Maria Zumbuehl, 2017. "The native-migrant gap in the progression into and through upper-secondary education," Economics of Education Working Paper Series 0139, University of Zurich, Department of Business Administration (IBW).
    16. Figueroa, Nicolás & Guadalupi, Carla, 2021. "Testing the sender: When signaling is not enough," Journal of Economic Theory, Elsevier, vol. 197(C).
    17. William Morrison & Dmitry Taubinsky, 2023. "Rules of Thumb and Attention Elasticities: Evidence from Under- and Overreaction to Taxes," The Review of Economics and Statistics, MIT Press, vol. 105(5), pages 1110-1127, September.
    18. Mikula, Stepan & Montag, Josef, 2023. "Roma and Bureaucrats: A Field Experiment on Ethnic and Socioeconomic Discrimination," IZA Discussion Papers 16218, Institute of Labor Economics (IZA).
    19. Chris Lam, 2024. "Debiasing Alternative Data for Credit Underwriting Using Causal Inference," Papers 2410.22382, arXiv.org, revised Oct 2024.
    20. Doris Weichselbaumer, 2020. "Multiple Discrimination against Female Immigrants Wearing Headscarves," ILR Review, Cornell University, ILR School, vol. 73(3), pages 600-627, May.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2102.10019. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.