做公平性AI或高利害决策系统的团队，这篇给出了一个可落地的方案——用专家评分嵌入替代黑盒特征，既减少偏见又提升质量，值得细读。
在招聘、大学录取等难以获得真实标签的领域，模型常依赖历史人工评估训练，但历史评估可能包含对特定群体的偏见。本文提出用专家定义的标准（评分嵌入）替代传统黑盒嵌入作为预测基础，使模型锚定在语义有意义的维度上，从而避免继承偏见。理论和实验证明，该方法在合理条件下能有效缓解标签偏差。在大型硕士项目申请数据集上，基于评分嵌入的模型减少了群体差异，同时提升了录取群体的整体质量。
Mitigating Label Bias with Interpretable Rubric Embeddings
Statistical decision algorithms are increasingly deployed in domains where ground-truth labels are hard to obtain, such as hiring, university admissions, and content moderation. In these settings, models are typically trained on historical human evaluations -- for example, using past hiring decisions as a proxy for true applicant quality. However, if past evaluations unjustly favor certain groups, models trained on these labels may inherit those biases. To address this problem, we propose basing predictions on rubric embeddings, a representation framework that replaces standard black-box embeddings with features derived from expert-defined criteria that align with the underlying construct of interest. By anchoring predictions to semantically meaningful dimensions, this approach guards against biased proxy signals. We provide both theoretical and empirical evidence that rubric embeddings mitigate label bias under plausible conditions. Empirically, we evaluate our method on a novel dataset of applications to a large master's program. We find that models trained on rubric embeddings reduce group disparities while improving measures of cohort quality. Our results suggest that basing predictions on interpretable, domain-grounded representations offers a practical approach to learning in the presence of biased labels.