Participatory Detection of Language Barriers towards Multilingual Sustainability(ies) in Africa

Abstract

After decades of political, economic, and scientific efforts, humanity has not gotten any closer to global sustainability. With less than a decade to reach the UN Sustainable Development Goals (SDGs) deadline of the 2030 Agenda, we show that global development agendas may be getting lost in translation, from their initial formulation to their final implementation. Sustainability science does not “speak” most of the 2000 languages from Africa, where the lack of indigenous terminology hinders global efforts such as the COVID-19 pandemic fight. Sociolinguistics, social psychology, cognitive ergonomics, geography, environmental sciences, and artificial intelligence (AI) are all relevant disciplinary fields to uncover the “foreign language effect” that hinders the implementation of the SDGs in Africa. We make the case for detecting and addressing language barriers towards multilingual sustainability in Africa by (1) exploring the ”foreign language effect” among African decision-makers and recognising their alternative social representations about sustainability; and (2) detecting Western language stereotypes about sustainability. We propose rethinking SDG-related scientific notions through participatory natural language processing (NLP) and the study of African social representations of sustainability, thus enabling a more inclusive and efficient approach to “sustainability(ies)”.