Background: The wide adoption of AI- and ML-based systems in sensitive domains raises severe concerns about their fairness. Many methods have been proposed in the literature to enhance software fairness. However, the majority behave as a black-box, not allowing stakeholders to prioritise fairness or effectiveness (i.e., prediction correctness) based on their needs. Aims: In this paper, we introduce FairRF, a novel approach based on multi-objective evolutionary search to optimise fairness and effectiveness in classification tasks. FairRF uses a Random Forest (RF) model as a base classifier and searches for the best hyperparameter configurations and data mutation to maximise fairness and effectiveness. Eventually, it returns a set of Pareto optimal solutions, allowing the final stakeholders to choose the best one based on their needs. Method: We conduct an extensive empirical evaluation of FairRF against 26 different baselines in 11 different scenarios using five effectiveness and three fairness metrics. Additionally, we also include two variations of the fairness metrics for intersectional bias for a total of six definitions analysed. Result: Our results show that FairRF can significantly improve the fairness of base classifiers, while maintaining consistent prediction effectiveness. Additionally, FairRF provides a more consistent optimisation under all fairness definitions compared to state-of-the-art bias mitigation methods and overcomes the existing state-of-the-art approach for intersectional bias mitigation. Conclusions: FairRF is an effective approach for bias mitigation also allowing stakeholders to adapt the development of fair software systems based on their specific needs.

翻译：背景：人工智能与机器学习系统在敏感领域的广泛应用引发了对其公平性的严重关切。文献中已提出多种提升软件公平性的方法。然而，大多数方法表现为黑箱，不允许利益相关者根据自身需求优先考虑公平性或有效性（即预测正确性）。目标：本文提出FairRF，一种基于多目标进化搜索的新方法，用于优化分类任务中的公平性与有效性。FairRF以随机森林模型作为基分类器，通过搜索最优超参数配置与数据变异来最大化公平性与有效性。最终，该方法返回一组帕累托最优解，使最终利益相关者能够根据需求选择最佳方案。方法：我们在11种不同场景下，使用五种有效性指标与三种公平性指标，对FairRF与26种基线方法进行了广泛实证评估。此外，我们还纳入两种针对交叉性偏见的公平性指标变体，共计分析了六种公平性定义。结果：实验表明，FairRF在保持稳定预测有效性的同时，能够显著提升基分类器的公平性。与最先进的偏见缓解方法相比，FairRF在所有公平性定义下均能提供更一致的优化效果，并在交叉性偏见缓解方面超越了现有最优方法。结论：FairRF是一种有效的偏见缓解方法，同时允许利益相关者根据具体需求调整公平软件系统的开发过程。