Fairness Improvement with Multiple Protected Attributes: How Far Are We?

Zhenpeng Chen, Jie M. Zhang*, Federica Sarro, Mark Harman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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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.
Original languageEnglish
Title of host publication46th International Conference on Software Engineering (ICSE 2024)
Publication statusAccepted/In press - 15 Oct 2023


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