Fairness audits of institutional risk models are critical for understanding how deployed machine learning pipelines allocate resources. Drawing on multi-year collaboration with Centennial College, where our prior ethnographic work introduced the ASP-HEI Cycle, we present a replica-based audit of a deployed Early Warning System (EWS), replicating its model using institutional training data and design specifications. We evaluate disparities by gender, age, and residency status across the full pipeline (training data, model predictions, and post-processing) using standard fairness metrics. Our audit reveals systematic misallocation: younger, male, and international students are disproportionately flagged for support, even when many ultimately succeed, while older and female students with comparable dropout risk are under-identified. Post-processing amplifies these disparities by collapsing heterogeneous probabilities into percentile-based risk tiers. This work provides a replicable methodology for auditing institutional ML systems and shows how disparities emerge and compound across stages, highlighting the importance of evaluating construct validity alongside statistical fairness. It contributes one empirical thread to a broader program investigating algorithms, student data, and power in higher education.

翻译：机构风险模型的公平性审计对于理解已部署机器学习流水线如何分配资源至关重要。基于与世纪学院多年合作（前期民族志研究引入了ASP-HEI循环），我们提出对已部署的早期预警系统进行基于复制的审计——使用机构训练数据和设计规格复现其模型。我们采用标准公平性指标评估整个流水线（训练数据、模型预测和后处理阶段）中由性别、年龄和居住状态导致的差异。审计揭示了系统性资源错配：年轻、男性及国际学生被不成比例地标记为需要支持（即便许多人最终成功毕业），而具有相似辍学风险的较大龄及女性学生则被低估识别。后处理阶段通过将异质性概率压缩为百分位风险等级进一步放大了这些差异。本研究为审计机构机器学习系统提供了可复现的方法论，并展示了差异如何在各阶段产生与累积，强调了评估结构效度与统计公平性并重的重要性。该工作为探讨算法、学生数据与高等教育权力关系的更宏观研究计划提供了一条实证脉络。