Auditing LLMs for Algorithmic Fairness in Casenote-Augmented Tabular Prediction

ArXi:2604.19204v1 Announce Type: cross LLMs are increasingly being considered for prediction tasks in high-stakes social service settings, but their algorithmic fairness properties in this context are poorly understood. In this short technical report, we audit the algorithmic fairness of LLM-based tabular classification on a real housing placement prediction task, augmented with street outreach casenotes from a nonprofit partner. We audit multi-class classification error disparities.