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Paper short abstract

This paper examines AI-driven revenue mobilisation in Kenya, through the integration of telco data with tax systems. It critiques tech-solutionist AI governance under Kenya’s Data Protection Act and AI Strategy, arguing that procedural compliance obscures political, and distributive challenges.

Paper long abstract

This paper critically examines tech-solutionism in AI governance through the empirical case of AI-driven revenue mobilisation in Kenya. Recently, the Kenyan government decided to integrate telecommunications and mobile money transactions data with Kenya Revenue Authority (KRA) systems as a strategy to enhance tax compliance, widen the tax base, and curb revenue leakages (Republic of Kenya, 2023). These initiatives are framed as neutral, efficiency-enhancing technological fixes to structural revenue challenges.

Considering Kenya’s Data Protection Act (2019) and the National Artificial Intelligence Strategy 2025–2030, the paper argues that such solutionist framings obscure the socio-political and distributive dimensions of taxation. While the Data Protection Act provides safeguards against solely automated decision-making and mandates data protection impact assessments (Republic of Kenya, 2019), in practice these mechanisms are operationalised as procedural compliance tools within revenue analytics systems. Application of predictive models trained on integrated data runs the danger of disproportionately targeting informal sector and intensifying surveillance without addressing underlying causes of informality.

The National AI Strategy positions AI on public sector efficiency and economic growth (Government of Kenya, 2024), reinforces a developmental narrative in which technological deployment precedes institutional readiness, and democratic oversight. This dynamic is conceptualised as epistemic displacement, whereby locally grounded understandings of informality, state–citizen trust, and fiscal justice are abandoned in favour of emerging technologies.

By foregrounding revenue mobilisation as a critical site of AI governance, the paper challenges ethical determinism and argues for a context-sensitive approach that treats AI governance as a political and distributive process rather than purely technical.