Artificial Intelligence Governance of Community Health Data Trusts Under Unmeasured Traditional Authority Consent Jurisdiction: A Healthcare Political Legitimacy Evaluation

Community health data trusts are being proposed across sub-Saharan Africa as vehicles for the secondary use of routine health information, yet the political legitimacy of the consent arrangements that would authorise them remains unmeasured wherever traditional authority exercises jurisdiction over the communities whose data are held. The framework specifies four legitimacy dimensions—authorisation, accountability, contestability and representational adequacy—and derives the conditions under which each is satisfied, strained or defeated when the boundaries of traditional authority jurisdiction are unknown. A before-after-control-impact design is characterised for the empirical evaluation of trust interventions, with a between-cluster standard deviation of 6.0, a residual standard deviation of 4.5 and an intraclass correlation of 0.64; power was estimated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effects of 3, 5 and 8 percentage points, and 54 clusters are required for 5 percentage points at 80 per cent power. Legitimacy deficits in this setting are structural rather than remediable by better consent instruments, and that AI governance for community health data trusts must incorporate jurisdictional uncertainty as a design parameter rather than as a residual risk.