发表机构
Stevens Institute of Technology（史蒂文斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多类多组分类的公平性挑战，提出基于相干风险测度的分类器设计及对应数值求解方法，该方法扩展性好且分类器鲁棒，在公平性处理上优于支持向量机等方法。
AI 中文摘要
针对存在向量值敏感属性的多类分类问题，我们提出一种公平分类器的新设计。在该场景中，每个敏感属性具有多个取值，形成若干与公平性考量相关的组，这些组自然存在重叠，需分析各因素间的交互作用。此外，借助分类器辅助决策的决策者不应以满足组级公平性指标为代价侵犯个体权利。我们提出一种基于相干风险测度理论与方法的方法，旨在解决公平性挑战，还提出一种专门的数值方法求解所得优化问题，该方法随观测数量增加具有良好扩展性。同时，我们注意到所得分类器对于损坏数据或数据稀缺的情况具有鲁棒性，通过与支持向量机框架及其他处理公平性的方法对比，证明了所提框架的优势。
英文摘要
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.
