FairRF: 多目标搜索用于单目标和交叉性软件公平性
FairRF: Multi-Objective Search for Single and Intersectional Software Fairness
AI总结：
FairRF通过多目标进化搜索优化分类任务中的公平性和有效性，提供帕累托最优解以适应不同需求。
摘要：
背景：人工智能和机器学习系统在敏感领域中的广泛应用引发了对其公平性的严重担忧。许多方法已被提出以提高软件公平性。然而，大多数方法表现为黑箱，不允许利益相关者根据其需求优先考虑公平性或有效性（即预测正确性）。目标：在本文中，我们介绍了FairRF，一种基于多目标进化搜索的新型方法，用于优化分类任务中的公平性和有效性。FairRF使用随机森林（RF）模型作为基础分类器，并搜索最佳超参数配置和数据变异以最大化公平性和有效性。最终，它会返回一组帕累托最优解，允许最终利益相关者根据其需求选择最佳方案。方法：我们对FairRF进行了广泛的实证评估，在11种不同场景中使用五个有效性指标和三个公平性指标，与26种不同的基线进行比较。此外，我们还包含两种公平性指标的变体，用于交叉性偏见，总计分析了六个定义。结果：我们的结果表明，FairRF可以显著提高基础分类器的公平性，同时保持一致的预测有效性。此外，FairRF在所有公平性定义下比最先进的偏见缓解方法提供了更一致的优化，并克服了现有的交叉性偏见缓解方法。结论：FairRF是一种有效的偏见缓解方法，也允许利益相关者根据其特定需求调整公平软件系统的开发。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
