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Data preprocessing techniques for classification without discrimination

机译：用于数据分类的数据预处理技术

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摘要

Recently, the following Discrimination-Aware Classification Problem was introduced: Suppose we are given training data that exhibit unlawful discrimination; e.g., toward sensitive attributes such as gender or ethnicity. The task is to learn a classifier that optimizes accuracy, but does not have this discrimination in its predictions on test data. This problem is relevant in many settings, such as when the data are generated by a biased decision process or when the sensitive attribute serves as a proxy for unobserved features. In this paper, we concentrate on the case with only one binary sensitive attribute and a two-class classification problem. We first study the theoretically optimal trade-off between accuracy and non-discrimination for pure classifiers. Then, we look at algorithmic solutions that preprocess the data to remove discrimination before a classifier is learned. We survey and extend our existing data preprocessing techniques, being suppression of the sensitive attribute, massaging the dataset by changing class labels, and reweighing or resampling the data to remove discrimination without relabeling instances. These preprocessing techniques have been implemented in a modified version of Weka and we present the results of experiments on real-life data.
机译：最近，引入了以下歧视意识分类问题：假设我们得到的训练数据表现出非法歧视;例如，针对敏感属性，例如性别或种族。任务是学习一个优化准确性的分类器，但在对测试数据的预测中不存在这种歧视。在许多情况下，例如当数据是由有偏见的决策过程生成的，或者当敏感属性用作未观察到的特征的代理时，此问题就很重要。在本文中，我们集中讨论只有一个二进制敏感属性和一个两类分类问题的情况。我们首先研究纯分类器的准确性和非歧视性之间的理论上的最佳折衷。然后，我们看一下在学习分类器之前对数据进行预处理以消除歧视的算法解决方案。我们调查并扩展了我们现有的数据预处理技术，包括抑制敏感属性，通过更改类标签按摩数据集，重新称重或重新采样数据以消除歧视，而无需重新标注实例。这些预处理技术已在Weka的修改版本中实现，我们介绍了真实数据的实验结果。

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