用风险度量方法设计多类别公平分类器，兼顾群体间交互与个体权利。
Fairness in multi-class multi-group classification problems via contextial coherent risk measures
- 基于一致风险度量构建公平分类框架，处理多敏感属性重叠群体。
- 在多个数据集上优于SVM等基准方法，且对噪声和小样本鲁棒。
- 适合需兼顾群体公平与个体权益的高风险决策场景。
我们提出一种针对具有向量型敏感属性的多类别分类问题的新公平分类器设计方法。在此场景中，每个敏感属性具有多个取值，形成多个与公平性相关的群体，这些群体自然存在重叠，需分析因素间的交互作用。此外，依赖分类结果做决策的主体不应以牺牲个体权利为代价来满足群体层面的公平指标。为此，我们采用一致风险度量的理论与方法来应对公平性挑战，并提出一种专用数值方法求解由此产生的优化问题。该方法在样本数量增加时仍能良好扩展。进一步地，所获分类器对数据污染或数据稀缺情况表现出强鲁棒性。我们在多个数据集上验证了该框架相对于支持向量机及其他公平性处理方法的优势。
原文摘要 · Abstract (English)
We propose a new design of fair classifiers for multi-class classification problems in the presence of vector-valued sensitive attributes. In that scenario each sensitive attribute has multiple values and forms several groups relevant to the fairness consideration. Naturally those groups are overlapping and one should also analyze the interaction of factors. Additionally, the decision makers aided by the classification should not violate individual rights at the expense of satisfying fairness metrics at the group level. We propose an approach using the theory and methods of coherent measures of risk aiming at resolving the fairness challenges. Further, we propose a specialized numerical method for solving the resulting optimization problem. The method scales well with the increase of the number of observations. Additionally, we note that the obtained classifier is robust with respect to corrupted data or to situation when data is scarce. We demonstrate the advantages of the proposed framework in comparison to the support-vector machine framework and other methods handling fairness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。