iFlipper: Label Flipping for Individual Fairness

As machine learning becomes prevalent, mitigating any unfairness present in the training data becomes critical. Among the various notions of fairness, this paper focuses on the well-known individual fairness, which states that similar individuals should be treated similarly. While individual fairness can be improved when training a model (in-processing), we contend that fixing the data before model training (pre-processing) is a more fundamental solution. In particular, we show that label flipping is an effective pre-processing technique for improving individual fairness.

Recommended citation: Hantian Zhang, Ki Hyun Tae, Jaeyoung Park, Xu Chu, and Steven Euijong Whang. 2023. IFlipper: Label Flipping for Individual Fairness. Proc. ACM Manag. Data 1, 1, Article 8 (May 2023), 26 pages. https://doi.org/10.1145/3588688.
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