Accurately measuring discrimination in machine learning-based automated decision systems is required to address the vital issue of fairness between subpopulations and/or individuals. Any bias in measuring discrimination can lead to either amplification or underestimation of the true value of discrimination. This paper focuses on a class of bias originating in the way training data is generated and/or collected. We call such class causal biases and use tools from the field of causality to formally define and analyze such biases. Four sources of bias are considered, namely, confounding, selection, measurement, and interaction. The main contribution of this paper is to provide, for each source of bias, a closed-form expression in terms of the model parameters. This makes it possible to analyze the behavior of each source of bias, in particular, in which cases they are absent and in which other cases they are maximized. We hope that the provided characterizations help the community better understand the sources of bias in machine learning applications.

翻译：准确测量基于机器学习的自动化决策系统中的歧视，是解决子群体和/或个体间公平性这一关键问题的必要条件。测量歧视中的任何偏差都可能导致真实歧视值的放大或低估。本文聚焦于由训练数据生成和/或收集方式产生的一类偏差。我们将此类偏差称为因果偏差，并利用因果关系领域的工具对其进行正式定义和分析。研究考虑了四种偏差来源，即混杂、选择、测量和交互。本文的主要贡献是为每种偏差来源提供了基于模型参数的闭合表达式。这使得分析每种偏差来源的行为成为可能，尤其是它们在哪些情况下不存在、在哪些情况下达到最大值。我们希望所提供的特征描述能帮助学界更好地理解机器学习应用中的偏差来源。