Fuzzy SWARA and Its Application to Prioritize the Artificial Intelligence-Based SWOT Factors for Achieving SDG 2 (Zero Hunger)

Abstract

This study adopts a fuzzy-based strategic approach to assess the strengths, weaknesses, opportunities, and threats (SWOT) associated with the role of artificial intelligence (AI) in achieving Sustainable Development Goal 2 (SDG 2: Zero Hunger). First, sixteen SWOT factors are identified through a comprehensive literature review and expert consultation. Subsequently, data were collected from four domain experts, and the Fuzzy Stepwise Weight Assessment Ratio Analysis (F-SWARA) method was applied to determine the relative importance of the identified SWOT factors. The findings reveal that the prediction of famine and crop-related issues using satellite and socioeconomic data (S2), together with AI-driven passive data for poverty insights (O5), are the most influential enablers of achieving zero hunger. In contrast, the shortage of relevant data for measuring poverty (W1) and the widening of the rich–poor gap due to automation (T1) constitute the major barriers to achieving this goal. The study contributes to the decision science and management literature by providing practical insights for policymakers seeking to accelerate progress toward SDG 2 and concludes by outlining several promising directions for future research.