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A fully Bayesian tracking algorithm for mitigating disparate prediction misclassification

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  • Short, Martin B.
  • Mohler, George O.

Abstract

We develop a fully Bayesian tracking algorithm with the purpose of providing classification prediction results that are unbiased when applied uniformly to individuals with differing sensitive variable values, e.g., of different races, sexes, etc. Here, we consider bias in the form of group-level differences in false prediction rates between the different sensitive variable groups. Given that the method is fully Bayesian, it is well suited for situations where group parameters or regression coefficients are dynamic quantities. We illustrate our method, in comparison to others, on simulated datasets and two real-world datasets.

Suggested Citation

  • Short, Martin B. & Mohler, George O., 2023. "A fully Bayesian tracking algorithm for mitigating disparate prediction misclassification," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1238-1252.
    • Handle: RePEc:eee:intfor:v:39:y:2023:i:3:p:1238-1252
    DOI: 10.1016/j.ijforecast.2022.05.008
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    References listed on IDEAS

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    1. Matias Barenstein, 2019. "ProPublica's COMPAS Data Revisited," Papers 1906.04711, arXiv.org, revised Jul 2019.
    2. Emma Pierson & Camelia Simoiu & Jan Overgoor & Sam Corbett-Davies & Daniel Jenson & Amy Shoemaker & Vignesh Ramachandran & Phoebe Barghouty & Cheryl Phillips & Ravi Shroff & Sharad Goel, 2020. "A large-scale analysis of racial disparities in police stops across the United States," Nature Human Behaviour, Nature, vol. 4(7), pages 736-745, July.
    3. Bakerman, Jordan & Pazdernik, Karl & Korkmaz, Gizem & Wilson, Alyson G., 2022. "Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest," International Journal of Forecasting, Elsevier, vol. 38(2), pages 648-661.
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