Existing research mostly improves the fairness of Machine Learning (ML)
software regarding a single protected attribute at a time, but this is
unrealistic given that many users have multiple protected attributes. This
paper conducts an extensive study of fairness improvement regarding multiple
protected attributes, covering 11 state-of-the-art fairness improvement
methods. We analyze the effectiveness of these methods with different datasets,
metrics, and ML models when considering multiple protected attributes. The
results reveal that improving fairness for a single protected attribute can
largely decrease fairness regarding unconsidered protected attributes. This
decrease is observed in up to 88.3% of scenarios (57.5% on average). More
surprisingly, we find little difference in accuracy loss when considering
single and multiple protected attributes, indicating that accuracy can be
maintained in the multiple-attribute paradigm. However, the effect on precision
and recall when handling multiple protected attributes is about 5 times and 8
times that of a single attribute. This has important implications for future
fairness research: reporting only accuracy as the ML performance metric, which
is currently common in the literature, is inadequate.

该研究论文对多个受保护属性的公平性改进进行了广泛研究，涵盖了 11 种最先进的公平性改进方法。结果显示，针对单个受保护属性改进公平性会大幅降低对未考虑的受保护属性的公平性。此外，在处理多个受保护属性时，精确率和召回率的影响是单个属性的 5 倍和 8 倍，在以准确性作为机器学习性能指标的将来公平性研究中，这有重要的影响。

多保护属性下公平性改善的实证研究

An Empirical Study on Fairness Improvement with Multiple Protected  Attributes

Increasingly, software is making autonomous decisions in case of criminal
sentencing, approving credit cards, hiring employees, and so on. Some of these
decisions show bias and adversely affect certain social groups (e.g. those
defined by sex, race, age, marital status). Many prior works on bias mitigation
take the following form: change the data or learners in multiple ways, then see
if any of that improves fairness. Perhaps a better approach is to postulate
root causes of bias and then applying some resolution strategy. This paper
postulates that the root causes of bias are the prior decisions that affect-
(a) what data was selected and (b) the labels assigned to those examples. Our
Fair-SMOTE algorithm removes biased labels; and rebalances internal
distributions such that based on sensitive attribute, examples are equal in
both positive and negative classes. On testing, it was seen that this method
was just as effective at reducing bias as prior approaches. Further, models
generated via Fair-SMOTE achieve higher performance (measured in terms of
recall and F1) than other state-of-the-art fairness improvement algorithms. To
the best of our knowledge, measured in terms of number of analyzed learners and
datasets, this study is one of the largest studies on bias mitigation yet
presented in the literature.

本研究拟定了 Fair-SMOTE 算法以解决自主决策中可能存在的偏见以及带来的问题，通过消除偏见标签、平衡内部分布使得基于敏感属性的示例在正负两类中均等地分布，从而提高了性能表现，并被证实是一种成功的偏差缓解方法。