FAHT: An Adaptive Fairness-aware Decision Tree Classifier
FAHT: An Adaptive Fairness-aware Decision Tree Classifier
复制标题
FAHT：自适应公平感知决策树分类器
DOI：
10.24963/ijcai.2019/205
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Eirini Ntoutsi
中科院分区：
文献类型：
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作者：
Wenbin Zhang;Eirini Ntoutsi
自动化数据驱动的决策系统在广泛的线上和线下服务中无处不在。这些系统依赖于复杂的学习算法和可用的数据，以优化决策支持辅助的服务功能。然而，由于现有的历史数据往往具有内在的歧视性，即在接受肯定分类时，具有一个或多个敏感属性的成员的比例高于总体比例，这导致决策支持系统缺乏公平性，因此人们越来越关注所采用模型的问责性和公平性。为了解决这一问题，已经提出了许多公平意识学习方法。然而，这些方法将公平性作为一个静态问题来处理，并且没有考虑潜在河流种群的演变。在本文中，我们引入了一种学习机制来为在线流决策设计一个公平的分类器。我们的学习模型FAHT(Fairness-Aware Hoeffding Tree)是著名的Hoeffding Tree算法的扩展，用于流上的决策树归纳，该算法也考虑了公平性。我们的实验表明，我们的算法能够处理流媒体环境中的歧视，同时保持对流媒体的适度预测性能。
Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well as offline services. These systems, depend on sophisticated learning algorithms and available data, to optimize the service function for decision support assistance. However, there is a growing concern about the accountability and fairness of the employed models by the fact that often the available historic data is intrinsically discriminatory, i.e., the proportion of members sharing one or more sensitive attributes is higher than the proportion in the population as a whole when receiving positive classification, which leads to a lack of fairness in decision support system. A number of fairness-aware learning methods have been proposed to handle this concern. However, these methods tackle fairness as a static problem and do not take the evolution of the underlying stream population into consideration. In this paper, we introduce a learning mechanism to design a fair classifier for online stream based decision-making. Our learning model, FAHT (Fairness-Aware Hoeffding Tree), is an extension of the well-known Hoeffding Tree algorithm for decision tree induction over streams, that also accounts for fairness. Our experiments show that our algorithm is able to deal with discrimination in streaming environments, while maintaining a moderate predictive performance over the stream.
DOI：
10.1145/3269206.3271717
发表时间：
2018-10
期刊：
Proceedings of the 27th ACM International Conference on Information and Knowledge Management
影响因子：
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作者：
Damianos P. Melidis;M. Spiliopoulou;Eirini Ntoutsi
通讯作者：
Damianos P. Melidis;M. Spiliopoulou;Eirini Ntoutsi