A Fully Bayesian, Logistic Regression Tracking Algorithm for Mitigating Disparate Misclassification

2020/09/24 by Martin B. Short, Short, Martin B., George Mohler +2
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #Applications (stat.AP) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #stat.AP

paper · pdf · doi:10.48550/arxiv.2012.00662

arxiv created 2020/09/24 · openalex publication_date 2020/09/24 · arxiv updated 2020/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We develop a fully Bayesian, logistic tracking algorithm with the purpose of providing classification results that are unbiased when applied uniformly to individuals with differing sensitive variable values. Here, we consider bias in the form of 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 logistic regression coefficients are dynamic quantities. We illustrate our method, in comparison to others, on both simulated datasets and the well-known ProPublica COMPAS dataset.

Citations

• Equality of Opportunity in Supervised Learning
• A statistical framework for fair predictive algorithms
• A Penalized Likelihood Method for Balancing Accuracy and Fairness in Predictive Policing

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