Predicting Priority Cases for Uruguay's Crece Contigo — An MIT-MIDES Fairness-Aware Model Still Under Evaluation

Uruguay's Ministry of Social Development (MIDES) runs Uruguay Crece Contigo, which supports pregnant women and children under four living in extreme poverty; caseworkers have historically prioritized referrals manually, risking inconsistency and bias.
MIT students (advised by a PhD candidate) worked with MIDES on anonymized data from over 15,000 historical referral cases, testing gradient-boosted trees, neural networks, LSTMs and ensemble methods, with fairness-aware techniques (SMOTE, RUS) specifically intended to reduce bias against under-represented groups.
As of mid-2025 the model remained a decision-support prototype under internal MIDES evaluation; no deployment date or performance figures have been published, making this an early-stage, honestly-unproven case rather than a demonstrated success story. It is nonetheless Uruguay's first documented attempt to apply machine learning to social-program targeting, and reflects the country's broader push — a national AI Observatory and AGESIC oversight — toward auditable government AI use.

Read the full analysis: https://misti.mit.edu/classroom-public-impact-how-mit-collaboration-helped-uruguay-harness-ai-social-policy