FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine
  Learning Software
Ying Xiao Shangwen Wang Sicen Liu Dingyuan Xue Xian Zhan Yepang Liu
Software built on top of machine learning algorithms is becoming increasingly
prevalent in a variety of fields, including college admissions, healthcare,
insurance, and justice. The effectiveness and efficiency of these systems
heavily depend on the quality of the training datasets. Biased datasets can
lead to unfair and potentially harmful outcomes, particularly in such critical
decision-making systems where the allocation of resources may be affected. This
can exacerbate discrimination against certain groups and cause significant
social disruption. To mitigate such unfairness, a series of bias-mitigating
methods are proposed. Generally, these studies improve the fairness of the
trained models to a certain degree but with the expense of sacrificing the
model performance. In this paper, we propose FITNESS, a bias mitigation
approach via de-correlating the causal effects between sensitive features
(e.g., the sex) and the label. Our key idea is that by de-correlating such
effects from a causality perspective, the model would avoid making predictions
based on sensitive features and thus fairness could be improved. Furthermore,
FITNESS leverages multi-objective optimization to achieve a better
performance-fairness trade-off. To evaluate the effectiveness, we compare
FITNESS with 7 state-of-the-art methods in 8 benchmark tasks by multiple
metrics. Results show that FITNESS can outperform the state-of-the-art methods
on bias mitigation while preserve the model's performance: it improved the
model's fairness under all the scenarios while decreased the model's
performance under only 26.67% of the scenarios. Additionally, FITNESS surpasses
the Fairea Baseline in 96.72% cases, outperforming all methods we compared.