2024-03-29T08:47:41Z
https://tsukuba.repo.nii.ac.jp/oai
oai:tsukuba.repo.nii.ac.jp:00048191
2022-04-27T09:18:34Z
152:2150
3:62:5586:7127
Model-based and actual independence for fairness-aware classification
佐久間, 淳
Kamishima, Toshihiro
Akaho, Shotaro
Asoh, Hideki
Sakuma, Jun
© The Author(s) 2017. This article is an open access publication
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The goal of fairness-aware classification is to categorize data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence. For example, when applying data mining technologies to university admissions, admission criteria must be non-discriminatory and fair with regard to sensitive features, such as gender or race. In this context, such fairness can be formalized as statistical independence between classification results and sensitive features. The main purpose of this paper is to analyze this formal fairness in order to achieve better trade-offs between fairness and prediction accuracy, which is important for applying fairness-aware classifiers in practical use. We focus on a fairness-aware classifier, Calders and Verwer’s two-naive-Bayes (CV2NB) method, which has been shown to be superior to other classifiers in terms of fairness. We hypothesize that this superiority is due to the difference in types of independence. That is, because CV2NB achieves actual independence, rather than satisfying model-based independence like the other classifiers, it can account for model bias and a deterministic decision rule. We empirically validate this hypothesis by modifying two fairness-aware classifiers, a prejudice remover method and a reject option-based classification (ROC) method, so as to satisfy actual independence. The fairness of these two modified methods was drastically improved, showing the importance of maintaining actual independence, rather than model-based independence. We additionally extend an approach adopted in the ROC method so as to make it applicable to classifiers other than those with generative models, such as SVMs.
Springer
2017
eng
journal article
http://hdl.handle.net/2241/00153681
https://tsukuba.repo.nii.ac.jp/records/48191
10.1007/s10618-017-0534-x
1384-5810
AA11127643
Data mining and knowledge discovery
32
1
258
286
https://tsukuba.repo.nii.ac.jp/record/48191/files/DMKD_32-258.pdf
application/pdf
1.0 MB
2018-11-13