Leveraging Natural Language Processing and Machine Learning for Evidence-Based Food Security Policy Decision-Making in Data-Scarce Making

TL;DR

This paper introduces ZeroHungerAI, a novel NLP and ML framework that leverages transfer learning and contextual embeddings to improve food security policy predictions in data-scarce regions, demonstrating high accuracy and fairness.

Contribution

The study presents a new integrated NLP and ML system using DistilBERT for evidence-based food security policy modeling under extreme data scarcity, outperforming classical models.

Findings

Achieved 91% classification accuracy on a hybrid dataset.

Outperformed SVM and Logistic Regression by 13% and 17%.

Reduced demographic bias to 3% parity difference.

Abstract

Food security policy formulation in data-scarce regions remains a critical challenge due to limited structured datasets, fragmented textual reports, and demographic bias in decision-making systems. This study proposes ZeroHungerAI, an integrated Natural Language Processing (NLP) and Machine Learning (ML) framework designed for evidence-based food security policy modeling under extreme data scarcity. The system combines structured socio-economic indicators with contextual policy text embeddings using a transfer learning based DistilBERT architecture. Experimental evaluation on a 1200-sample hybrid dataset across 25 districts demonstrates superior predictive performance, achieving 91 percent classification accuracy, 0.89 precision, 0.85 recall, and an F1 score of 0.86 under imbalanced conditions. Comparative analysis shows a 13 percent performance improvement over classical SVM and 17 percent over Logistic Regression models. Precision Recall evaluation confirms robust minority class detection (average precision around 0.88). Fairness aware optimization reduces demographic parity difference to 3 percent, ensuring equitable rural urban policy inference. The results validate that transformer based contextual learning significantly enhances policy intelligence in low resource governance environments, enabling scalable and bias aware hunger prediction systems.