通过生成高阶输入变异体提升模型在多重属性下的公平性
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software
- 用同一模型生成多种输入变异体，融合决策以增强公平性
- 平均提升47.5%的交叉公平性，优于当前最优方法9.6个百分点
- 无需重新训练，适合已部署模型的公平性优化
交叉公平性是机器学习软件的关键要求，需在多个受保护属性定义的子群体间实现公平。本文提出FairHOME，一种基于输入高阶变异的集成方法，在推理阶段提升机器学习软件的交叉公平性。受社会科学中多样性优势理论启发，FairHOME为每个输入实例生成代表多样化子群体的变异体，从而拓宽决策视角，促进更公平的判断。与传统集成方法结合不同模型预测不同，FairHOME将同一模型对原始输入及其变异体的预测结果融合，做出最终决策。特别地，该方法适用于已部署的机器学习系统，无需训练新模型。我们在24个典型决策任务上，采用广泛使用的指标，对FairHOME与七种前沿公平性改进方法进行对比评估。结果显示，FairHOME在所有指标上均持续优于现有方法，平均提升交叉公平性47.5%，超过当前最优方法9.6个百分点。
原文摘要 · Abstract (English)
Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes. This paper introduces FairHOME, a novel ensemble approach using higher order mutation of inputs to enhance intersectional fairness of ML software during the inference phase. Inspired by social science theories highlighting the benefits of diversity, FairHOME generates mutants representing diverse subgroups for each input instance, thus broadening the array of perspectives to foster a fairer decision-making process. Unlike conventional ensemble methods that combine predictions made by different models, FairHOME combines predictions for the original input and its mutants, all generated by the same ML model, to reach a final decision. Notably, FairHOME is even applicable to deployed ML software as it bypasses the need for training new models. We extensively evaluate FairHOME against seven state-of-the-art fairness improvement methods across 24 decision-making tasks using widely adopted metrics. FairHOME consistently outperforms existing methods across all metrics considered. On average, it enhances intersectional fairness by 47.5%, surpassing the currently best-performing method by 9.6 percentage points.
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