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引用次数: 6

摘要

机器学习的最新进展导致了处理数据中存在的不同类型偏见的新方法的出现。一方面,反事实学习处理用于采样(或记录)数据的策略中的偏差,以便评估和学习新策略。另一方面,公平意识学习旨在学习公平模式,以避免对某些个人或群体的歧视。在本文中,我们设计了一个反事实框架来模拟公平感知学习,这种学习受益于反事实推理来实现更公平的决策支持系统。我们利用公平性的定义来确定反事实设置中的强盗反馈,该设置从离线数据中学习分类策略,并平衡分类性能与公平性度量。在实验中,我们证明了与已知的基线系统相比,反事实设置可以完美地用于学习具有竞争性结果的公平模型。
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Fair Classification with Counterfactual Learning
Recent advances in machine learning have led to emerging new approaches to deal with different kinds of biases that exist in the data. On the one hand, counterfactual learning copes with biases in the policy used for sampling (or logging) the data in order to evaluate and learn new policies. On the other hand, fairness-aware learning aims at learning fair models to avoid discrimination against certain individuals or groups. In this paper, we design a counterfactual framework to model fairness-aware learning which benefits from counterfactual reasoning to achieve more fair decision support systems. We utilize a definition of fairness to determine the bandit feedback in the counterfactual setting that learns a classification strategy from the offline data, and balances classification performance versus fairness measure. In the experiments, we demonstrate that a counterfactual setting can be perfectly exerted to learn fair models with competitive results compared to a well-known baseline system.
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