How natural language processing and AI can help policymakers address global food insecurity
Natural language processing (NLP), a subfield of artificial intelligence that uses computational techniques to interpret, analyze, and generate human language, encompasses a range of tasks and techniques.
- Artificial Intelligence
- natural language processing
- food security
By Marieke MeeskeFebruary 12, 2026
Natural language processing (NLP), a subfield of artificial intelligence that uses computational techniques to interpret, analyze, and generate human language, encompasses a range of tasks and techniques. These include the large language models (LLMs) that power chatbots and other types of systems, as well as specific approaches (some employed by LLMs), including information extraction and text mining.
NLP offers powerful opportunities to support the UN Sustainable Development Goals (SDGs)—including SDG2 (Zero Hunger). In the wake of the COVID-19 pandemic, the Russia-Ukraine war, mounting climate change impacts, and other crises in the 2020s, global food security has suffered and progress towards meeting SDG2 has lagged. Urgent action, backed by evidence-based policymaking, is needed to reverse this trend.
NLP applications in the policy cycle can help to address food insecurity and meet SDG2. In recent decades, there has been a significant increase in the volume of unstructured data generated from diverse sources, including social media, research publications, and news articles. Due to its complexity and volume, traditional methods of data analysis are often insufficient to extract actionable insights for evidence-based policymaking; NLP tools can greatly expand the capacity to gain insights from such data, thereby contributing to the policy process.