  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 F1-score
when handling two protected attributes is about twice 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.
