FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software

Abstract

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.