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A fully Bayesian tracking algorithm for mitigating disparate prediction misclassification
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DOI：10.1016/j.ijforecast.2022.05.008.png)
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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.& COPY; 2022 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Bayesian
Tracking algorithm
Fair algorithm
Bias reduction
Recidivism
期刊
IF:
7.1
论文数:
3.1K
被引数:
9.9K
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