MULTILINGUAL AI FOR GLOBAL SUSTAINABILITY: ADDRESSING LANGUAGE BARRIERS IN ACHIEVING THE SDGS

Abstract

The Sustainable Development Goals (SDGs) provide a universal blueprint for addressing global challenges, including poverty, inequality, health, and education, by 2030. However, language barriers pose a significant obstacle to inclusive and equitable participation in achieving these goals, particularly for linguistically diverse and marginalized communities. This research investigates the role of multilingual artificial intelligence (AI) in mitigating these barriers to accelerate progress toward the SDGs. By synthesizing recent advancements in natural language processing (NLP) and evaluating multilingual AI applications across various sectors, this study provides insights into the potential and limitations of these technologies. A mixed-methods approach, combining qualitative and quantitative analyses, is employed to assess case studies and gather stakeholder perspectives. The findings suggest that multilingual AI has transformative potential but also highlights challenges such as algorithmic bias, data scarcity, and implementation gaps. Recommendations are proposed to maximize the effectiveness of multilingual AI in global sustainability efforts.