文章基本信息

标题：Fairness-Aware Decision Tree Editing Based on Mixed- Integer Linear Optimization
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作者：Kentaro Kanamori ; Hiroki Arimura
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2021
卷号：36
期号：4
页码：1-10
DOI：10.1527/tjsai.36-4_B-L13
语种：Japanese
出版社：The Japanese Society for Artificial Intelligence
摘要：In the application of machine learning models to decision-making tasks (e.g., loan approval), fairness of their predictions has emerged as an important topic in recent years. If decision-makers detect unfairness in their models during deployment, they must modify the models to satisfy constraints on a specific discrimination criterion. However, simply retraining a model from scratch under fairness constraints may raise serious reliability issues caused by differences in prediction and interpretation between the initial model and retrained model. In this paper, we propose a post-processing framework, named Fairness-Aware Decision tree Editing (FADE), that converts a given biased decision tree into a fair decision tree without significantly changing it in terms of its prediction and interpretation. For this purpose, we introduce two dissimilarity measures between decision trees based on the prediction discrepancy and edit distance. We propose a mixed-integer linear optimization formulation for minimizing the dissimilarity measures under fairness constraints. Numerical experiments on real datasets demonstrate the effectiveness of our method in comparison with existing methods.
关键词：fairness in machine learning;decision trees;mixed-integer linear optimization