A THREE-STAGE FAIRNESS-AWARE FRAMEWORK FOR RISK ASSESSMENT IN SOCIAL WORK: BALANCING ACCURACY AND EQUITY IN PREDICTIVE MODELS
TL;DR
This study addresses the critical trade-off between predictive accuracy and algorithmic fairness and proposes a “Three-Stage Fairness-Aware Framework” integrating pre-processing, in-processing, and post-processing mitigation strategies that successfully reduced bias to ethical thresholds.
Abstract
The integration of Artificial Intelligence (AI) into high-stakes social work, such as child welfare and criminal justice, promises efficiency but risks perpetuating systemic biases. This study addresses the critical trade-off between predictive accuracy and algorithmic fairness. Baseline evaluations of Extreme Gradient Boosting (XGBoost) models across three datasets (COMPAS, AFST, and a synthetic dataset) revealed strong predictive performance (AUC up to 0.89) but significant racial bias. As shown in the fairness metrics comparison, pre-mitigation Statistical Parity Difference (SPD) values indicated severe bias (COMPAS: –0.21, AFST: –0.15, Synthetic: –0.18). To resolve this, we propose a “Three-Stage Fairness-Aware Framework” integrating pre-processing, in-processing, and post-processing mitigation strategies. The application of this framework successfully reduced bias to ethical thresholds, with post-mitigation SPD values improving significantly to –0.07, –0.04 and –0.06, respectively, while incurring minimal accuracy loss (< 5.1% AUC reduction). Furthermore, the study validates the framework’s computational scalability and its alignment with strict regulatory standards like the EU AI Act and GDPR. These findings provide an evidence-based blueprint for equitable and legally compliant AI governance in public services.