Biases with respect to socially-salient attributes of individuals have been well documented in evaluation processes used in settings such as admissions and hiring. We view such an evaluation process as a transformation of a distribution of the true utility of an individual for a task to an observed distribution and model it as a solution to a loss minimization problem subject to an information constraint. Our model has two parameters that have been identified as factors leading to biases: the resource-information trade-off parameter in the information constraint and the risk-averseness parameter in the loss function. We characterize the distributions that arise from our model and study the effect of the parameters on the observed distribution. The outputs of our model enrich the class of distributions that can be used to capture variation across groups in the observed evaluations. We empirically validate our model by fitting real-world datasets and use it to study the effect of interventions in a downstream selection task. These results contribute to an understanding of the emergence of bias in evaluation processes and provide tools to guide the deployment of interventions to mitigate biases.

翻译：个体因社会显著性特征而产生的偏见，在招生、招聘等场景的评估过程中已有充分记录。我们将此类评估过程视为个体对某项任务真实效用分布向观测分布的转化，并将其建模为受信息约束下的损失最小化问题的解。该模型包含两个已被确认为导致偏见的关键参数：信息约束中的资源-信息权衡参数与损失函数中的风险规避参数。我们刻画了该模型产生的分布特征，并研究参数对观测分布的影响。模型输出丰富了可用于捕捉观测评估中群体差异的分布类型。我们通过拟合真实数据集进行实证验证，并利用该模型研究下游选择任务中干预措施的效果。这些成果有助于理解评估过程中偏见的形成机制，并为指导实施偏见缓解干预措施提供工具。