Fairness Audits of Institutional Risk Models in Deployed ML Pipelines
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 disproportion...
| Authors | Kelly McConvey, Dipto Das, Maya Ghai et al. |
|---|---|
| Primary category | cs.CY |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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