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When Algorithms Import Private Bias into Public Enforcement: The Promise and Limitations ofStatistical Debiasing Solutions

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  • Kristen M. Altenburger
  • Daniel E. Ho

Abstract

We make two contributions to understanding the role of algorithms in regulatory enforcement. First, we illustrate how big-data analytics can inadvertently import private biases into public policy. We show that a much-hyped use of predictive analytics - using consumer data to target food-safety enforcement - can disproportionately harm Asian establishments. Second, we study a solution by Pope and Sydnor (2011), which aims to debias predictors via marginalization, while still using information of contested predictors. We find the solution may be limited when protected groups have distinct predictor distributions, due to model extrapolation. Common machine-learning techniques heighten these problems.

Suggested Citation

  • Kristen M. Altenburger & Daniel E. Ho, 2019. "When Algorithms Import Private Bias into Public Enforcement: The Promise and Limitations ofStatistical Debiasing Solutions," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 175(1), pages 98-122.
    • Handle: RePEc:mhr:jinste:urn:doi:10.1628/jite-2019-0001
    DOI: 10.1628/jite-2019-0001
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    More about this item

    Keywords

    racial bias; antidiscrimination; predictive targeting; algorithmic fairness;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • K23 - Law and Economics - - Regulation and Business Law - - - Regulated Industries and Administrative Law
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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