Differences in data distributions between demographic groups, known as the problem of infra-marginality, complicate how people evaluate fairness in machine learning models. We present a user study with 85 participants in a hypothetical medical decision-making scenario to examine two treatments: group-specific model performance and training data availability. Our results show that participants did not equate fairness with simple statistical parity. When group-specific performances were equal or unavailable, participants preferred models that produced equal outcomes; when performances differed, especially in ways consistent with data imbalances, they judged models that preserved those differences as more fair. These findings highlight that fairness judgments are shaped not only by outcomes, but also by beliefs about the causes of disparities. We discuss implications for popular group fairness definitions and system design, arguing that accounting for distributional context is critical to aligning algorithmic fairness metrics with human expectations in real-world applications.

翻译：人口群体间数据分布的差异（即边际内差异问题）使人们评估机器学习模型公平性的过程变得复杂。我们在一个假设的医疗决策场景中对85名参与者开展用户研究，考察两种处理方式：群体特定模型性能与训练数据可用性。结果表明，参与者并未将公平性与简单的统计等同性划等号。当群体特定性能相等或不可获取时，参与者倾向于选择产生平等结果的模型；当性能存在差异时（特别是与数据不平衡状况相符的差异），参与者认为保持这些差异的模型更为公平。这些发现表明，公平性判断不仅受结果影响，还受到对差异成因的信念所塑造。我们讨论了主流群体公平性定义与系统设计的影响，主张在现实应用中必须考虑分布背景，才能使算法公平性指标与人类期望保持一致。