论文标题
人口统计学中机器学习算法的公平性
Fairness of Machine Learning Algorithms in Demography
论文作者
论文摘要
该论文致力于研究俄罗斯人群数据集的模型公平和过程公平性，通过预测第一婚姻，宗教信仰，第一工作和完成教育的离婚。我们的目标是通过减少对敏感功能的依赖，同时提高或至少保持其准确性，使分类器更加公平。我们从基于神经的方法中的“辍学”技术中汲取了灵感，并提出了一种使用“功能辍学”来解决过程公平性的模型。为了评估分类器的公平性并确定消除敏感的功能，我们使用了“石灰解释”。这导致由于特征辍学而导致的分类器池，其合奏已被证明较少依赖敏感特征，并且对准确性的改善或没有影响。 Our empirical study was performed on four families of classifiers (Logistic Regression, Random Forest, Bagging, and Adaboost) and carried out on real-life dataset (Russian demographic data derived from Generations and Gender Survey), and it showed that all of the models became less dependent on sensitive features (such as gender, breakup of the 1st partnership, 1st partnership, etc.) and showed improvements or no impact on accuracy
The paper is devoted to the study of the model fairness and process fairness of the Russian demographic dataset by making predictions of divorce of the 1st marriage, religiosity, 1st employment and completion of education. Our goal was to make classifiers more equitable by reducing their reliance on sensitive features while increasing or at least maintaining their accuracy. We took inspiration from "dropout" techniques in neural-based approaches and suggested a model that uses "feature drop-out" to address process fairness. To evaluate a classifier's fairness and decide the sensitive features to eliminate, we used "LIME Explanations". This results in a pool of classifiers due to feature dropout whose ensemble has been shown to be less reliant on sensitive features and to have improved or no effect on accuracy. Our empirical study was performed on four families of classifiers (Logistic Regression, Random Forest, Bagging, and Adaboost) and carried out on real-life dataset (Russian demographic data derived from Generations and Gender Survey), and it showed that all of the models became less dependent on sensitive features (such as gender, breakup of the 1st partnership, 1st partnership, etc.) and showed improvements or no impact on accuracy
