Adopting artificial intelligence for sustainable development : examples of science diplomacy during the COVID-19 pandemic

Abstract

The preliminary observation that inspires this study is that many countries are held back by data capacity deficits and incapacity in diagnosing spatial distribution of socioeconomic vulnerabilities. This has manifested into inability to identify the socioeconomically vulnerable and the inability to prioritise the resources to the most vulnerable households. This study seeks to demonstrate concrete applicability of science diplomacy theory through adoption of Artificial Intelligence (AI) for sustainable development against the socioeconomic manifestations of Covid 19 in Togo. The study finds that supervised Machine Learning Algorithms and satellite Imageries were accurate in mapping poverty distribution across 100 poorest cantons as well as to identify small pocket of poverty within the generally wealthy geographic coverages. However, the bearers of poverty are people not geographic locations. To target specific poorest individual Call Detail Records of the mobile phone use features were adopted to determine parsimony levels as the reflection of poverty status. Based on the observation on accuracy of cash transfers, the study invokes the inferences and implications on the progress towards target 17.18 addressing availability of reliable data as the starting point for locating the cross-target synergies and trade-offs which are requisite for tapping into the transformative potential of the sustainable development agenda.