Machine Learning Prediction of Educational Disparities in Somaliland with Algorithmic Fairness Evaluation

This study, "Predicting Educational Disparity in Somaliland: A Machine Learning Analysis and Algorithmic Fairness Audit," aims to identify the primary drivers of educational disparity and assess the equity of a predictive model in a fragile, post-conflict context. Utilizing microdata from the 2020 Somaliland Health and Demographic Survey (SLHDS) for a sample of 17,686 women, we developed a Random Forest classification model to predict school attendance. The model achieved excellent predictive accuracy, with a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of 0.988, identifying spousal education and household wealth as the most significant predictors. However, a subsequent algorithmic fairness audit revealed a critical Equal Opportunity gap of 18.3 percentage points between the best- and worst-performing regions, indicating the model is significantly less effective for women in the Sool region. This finding demonstrates that even highly accurate models can perpetuate systemic inequities. For policy, this implies that deploying AI tools without rigorous fairness evaluations risks exacerbating marginalization; therefore, fairness audits must be a mandatory component of data-driven policymaking in fragile states to ensure interventions are both effective and equitable.