Academic

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

arXiv:2603.20425v1 Announce Type: new 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 Reca

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Karan Kumar Singh, Nikita Gajbhiye · · 1 min read · 3 views
#cs.AI

arXiv:2603.20425v1 Announce Type: new 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.

Executive Summary

This study presents ZeroHungerAI, an innovative framework combining Natural Language Processing (NLP) and Machine Learning (ML) to inform evidence-based food security policy decision-making in data-scarce regions. Leveraging a transfer learning based DistilBERT architecture, the system integrates structured socio-economic indicators with contextual policy text embeddings, achieving superior predictive performance and robust minority class detection. The results demonstrate the potential of transformer-based contextual learning in enhancing policy intelligence in low-resource governance environments. However, the study's focus on a specific context and dataset limits its generalizability. Nevertheless, the findings have significant implications for scalable and bias-aware hunger prediction systems.

Key Points

  • ▸ ZeroHungerAI framework combines NLP and ML for evidence-based food security policy decision-making
  • ▸ Transfer learning based DistilBERT architecture achieves superior predictive performance
  • ▸ Robust minority class detection and fairness-aware optimization ensure equitable rural-urban policy inference

Merits

Strength in Addressing Data Scarcity

ZeroHungerAI effectively tackles the challenge of data scarcity in food security policy formulation by leveraging NLP and ML.

Improved Policy Intelligence

The framework's use of transformer-based contextual learning significantly enhances policy intelligence in low-resource governance environments.

Scalability and Bias-Awareness

ZeroHungerAI enables scalable and bias-aware hunger prediction systems, ensuring equitable rural-urban policy inference.

Demerits

Limited Generalizability

The study's focus on a specific context and dataset limits its generalizability to other regions and settings.

Dependence on High-Quality Training Data

The framework's performance may be compromised by the quality and availability of training data, particularly in data-scarce regions.

Expert Commentary

While ZeroHungerAI represents a significant advancement in addressing data scarcity in food security policy formulation, its limitations and potential biases must be carefully considered. Future research should focus on further developing the framework's generalizability, scalability, and adaptability to diverse contexts. Additionally, the study's findings should be contextualized within existing literature on data-driven policy approaches and the application of NLP and ML in policy decision-making. By doing so, ZeroHungerAI can be positioned as a valuable tool for informing evidence-based policy decisions in low-resource governance environments.

Recommendations

  • ✓ Recommendation 1: Further research should be conducted to evaluate the framework's performance in diverse contexts and datasets, ensuring its generalizability and adaptability to different settings.
  • ✓ Recommendation 2: Policymakers and practitioners should be engaged in the development and implementation of ZeroHungerAI to ensure the framework's relevance and effectiveness in addressing food security challenges in data-scarce regions.

Sources

Original: arXiv - cs.AI

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