Unaware Fairness: Hierarchical Random Forest for Protected Classes

2021/06/30 by Xian Li, Li, Xian
Computer Science · Mathematics · #Applications (stat.AP) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.CY #cs.LG #stat.AP

paper · pdf · doi:10.48550/arxiv.2106.15767

arxiv created 2021/06/30 · arxiv updated 2021/07/01

Abstract

Procedural fairness has been a public concern, which leads to controversy when making decisions with respect to protected classes, such as race, social status, and disability. Some protected classes can be inferred according to some safe proxies like surname and geolocation for the race. Hence, implicitly utilizing the predicted protected classes based on the related proxies when making decisions is an efficient approach to circumvent this issue and seek just decisions. In this article, we propose a hierarchical random forest model for prediction without explicitly involving protected classes. Simulation experiments are conducted to show the performance of the hierarchical random forest model. An example is analyzed from Boston police interview records to illustrate the usefulness of the proposed model.

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