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- W3175493710 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." @default.
- W3175493710 created "2021-07-05" @default.
- W3175493710 creator A5058358879 @default.
- W3175493710 date "2021-06-30" @default.
- W3175493710 modified "2023-10-16" @default.
- W3175493710 title "Unaware Fairness: Hierarchical Random Forest for Protected Classes." @default.
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- W3175493710 hasPublicationYear "2021" @default.
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