Although many fairness criteria have been proposed to ensure that machine learning algorithms do not exhibit or amplify our existing social biases, these algorithms are trained on datasets that can themselves be statistically biased. In this paper, we investigate the robustness of a number of existing (demographic) fairness criteria when the algorithm is trained on biased data. We consider two forms of dataset bias: errors by prior decision makers in the labeling process, and errors in measurement of the features of disadvantaged individuals. We analytically show that some constraints (such as Demographic Parity) can remain robust when facing certain statistical biases, while others (such as Equalized Odds) are significantly violated if trained on biased data. We also analyze the sensitivity of these criteria and the decision maker's utility to biases. We provide numerical experiments based on three real-world datasets (the FICO, Adult, and German credit score datasets) supporting our analytical findings. Our findings present an additional guideline for choosing among existing fairness criteria, or for proposing new criteria, when available datasets may be biased.
翻译:虽然提出了许多公平标准,以确保机器学习算法不表现出或扩大我们现有的社会偏见,但这些算法在数据集方面受过培训,而这些数据集本身可能具有统计上的偏差。在本文件中,我们调查在对算法进行有偏差的数据培训时,现有的(人口)公平标准是否健全。我们考虑了两种形式的数据集偏差:先前的决策者在标签过程中的错误,以及衡量弱势个人特征方面的错误。我们的分析表明,在面对某些统计偏见时,某些制约因素(如人口均等)仍然可以保持稳健,而其他制约因素(如偶数)则如果在有偏差的数据方面受过培训,则会大大违反。我们还分析了这些标准的敏感性和决策者对偏差的效用。我们提供了基于三个真实世界数据集(FICO、成人和德国信用分数数据集)的数值实验,以支持我们的分析结论。我们的调查结果为在现有的公平标准中作出选择或提出新的标准提供了额外的准则,如果现有的数据集可能有偏差的话。