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.
Abstract (translated)
在数据稀缺地区制定粮食安全政策仍是一项关键挑战，原因在于结构化数据集有限、文本报告碎片化以及决策系统中存在人口统计偏差。本研究提出ZeroHungerAI——一个集成自然语言处理（NLP）与机器学习（ML）的框架，专为极端数据稀缺条件下的循证粮食安全政策建模而设计。该系统通过基于迁移学习的DistilBERT架构，将结构化社会经济指标与情境化政策文本嵌入相结合。在涵盖25个地区、共1200个样本的混合数据集上的实验评估表明，该框架在不平衡条件下展现出卓越的预测性能，分类准确率达91%，精确率为0.89，召回率为0.85，F1分数为0.86。对比分析显示，其性能较经典支持向量机（SVM）提升13%，较逻辑回归模型提升17%。精确率-召回率评估证实了其对少数类样本的稳健检测能力（平均精确率约0.88）。通过公平性感知优化，人口统计均等差异降至3%，确保了城乡政策推论的公平性。研究结果验证了基于Transformer的情境化学习能显著提升资源匮乏治理环境中的政策智能，为实现可扩展且具备偏差意识的饥饿预测系统提供了可能。
URL
https://arxiv.org/abs/2603.20425
