Reimagining Multidimensional Child Poverty Solutions in Kagera, Tanzania: The Role of Artificial Intelligence Within a Bioecological Systems Framework
Abstract
Multidimensional child poverty in Tanzania’s Kagera Region affects nearly half of the children, with 44% living with three or more deprivations in housing, water and sanitation, education, health, nutrition, information and communication, and protection. This narrative review explains how Artificial Intelligence (AI) could be a hope and help in solving these deep-seated issues. Following evidence identified in peer-reviewed databases and grey literature, the review employs Bronfenbrenner’s Bioecological Systems Theory (Process–Person–Context–Time (PPCT) framework) to guide the discussion. Results show that AI holds immense potential for predictive modelling of high-risk household contexts and service delivery gaps (Process), early risk identification of health and nutrition (Person), strengthening education, health, and protection systems (Context), and monitoring patterns of poverty across generations and assessing the long-term impact of interventions (Time). However, by embracing AI, we also need to be sensitive to the ethical issues that come with it. These include addressing issues like data privacy, building trust among the populace, fighting biases in algorithms, and ensuring fair access to technological infrastructure. This study emphasizes the need for building AI systems that are participatory, child-needs-oriented and premised on the contexts of the communities where they are to be used. In doing so, AI will hold transformative potential to disrupt poverty cycles in Kagera and beyond.
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