Contextualizing AI Ethics in Uganda Through Adaptive Sensitive Reweighting (ASR) for Equitable Microcredit

Abstract

Abstract This research tackles the pressing ethical concerns of using AI in Uganda’s microcredit sector, namely to develop an adaptive sensitive reweighting (ASR) model to mitigate algorithmic bias and promote equitable access to credit. Traditional credit scoring models— and fairness-aware machine learning algorithms trained on Western-biased data—discriminate against marginalized groups because they are based on formal financial records, reinforcing structural disadvantages. By iterative engagement with Ugandan policymakers, lenders, borrowers, and AI experts, the most significant ethical concerns and context-specific fairness metrics were identified. The ASR approach adaptively adjusts weights for sensitive features such as collateral values and transaction history during model training to enhance fairness. Experimental outcomes on a typical credit scoring dataset demonstrate ASR’s success: the inclusion rate of disadvantaged borrowers is enhanced by 15% with predictive accuracy maintained and key fairness metrics significantly improved. The research provides actionable policy recommendations on implementing ASR-based AI systems in Uganda’s microfinance sector to drive financial inclusion and sustainable development. This study contributes to emerging Majority World scholarship on AI ethics by demonstrating the necessity of situating ethical frameworks and valuing stakeholder perspectives to develop equitable, inclusive AI systems. Our findings offer valuable insights for policymakers, microfinance institutions, and AI practitioners who aim to implement responsible AI in developing economies.

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