The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern. Addressing these fairness bugs often involves sacrificing ML performance, such as accuracy. To address this issue, we present a novel counterfactual approach that uses counterfactual thinking to tackle the root causes of bias in ML software. In addition, our approach combines models optimized for both performance and fairness, resulting in an optimal solution in both aspects. We conducted a thorough evaluation of our approach on 10 benchmark tasks using a combination of 5 performance metrics, 3 fairness metrics, and 15 measurement scenarios, all applied to 8 real-world datasets. The conducted extensive evaluations show that the proposed method significantly improves the fairness of ML software while maintaining competitive performance, outperforming state-of-the-art solutions in 84.6% of overall cases based on a recent benchmarking tool.

翻译：机器学习（ML）软件日益广泛的应用可能导致不公平且不道德的决策，因此软件中的公平性缺陷正成为一个日益受关注的问题。解决这些公平性缺陷通常需要以牺牲机器学习性能（例如准确率）为代价。针对这一问题，我们提出了一种新颖的反事实方法，该方法利用反事实思维来应对机器学习软件中偏见的根本成因。此外，我们的方法结合了针对性能和公平性均进行优化的模型，从而在这两方面都获得了最优解。我们结合使用了5种性能指标、3种公平性指标以及15种测量场景，在8个真实世界数据集上对10项基准任务进行了全面评估。所进行的广泛评估表明，所提出的方法在保持具有竞争力的性能的同时，显著提升了机器学习软件的公平性；基于一项最新的基准测试工具，该方法在84.6%的总体案例中优于当前最先进的解决方案。