Artificial Intelligence Governance of Clinical Model Retraining Mandates Under Unmeasured Regulator-Developer Information Asymmetry: A Healthcare Political Bargaining Evaluation

Clinical artificial intelligence governance increasingly relies on retraining mandates: rules requiring developers to update deployed models when performance drifts. Regulators issue these mandates without direct access to the developer's private information about model stability, data provenance and retraining cost. the present analysis treats the retraining mandate as a political bargaining instrument rather than a technical specification, and asks what happens to the credibility of that instrument when the regulator cannot observe the developer's type. A formal bargaining model is developed in which the regulator chooses a mandate threshold and the developer chooses whether to comply, contest or exit, under unmeasured information asymmetry. The model is evaluated through a replication-based design study of the before-after-control-impact estimator, using a declared variance structure with between-cluster SD of 6.0, residual SD of 4.5 and intraclass correlation of 0.64, across 2000 replications per design cell. The analysis characterises the operating characteristics of the design rather than reporting observational estimates. The design study indicates that 54 clusters are required to detect a 5 percentage point effect at 80% power under the declared variance structure. Retraining mandates function as separating devices only when the regulator's verification technology is at least as informative as the developer's private signal, and that in the absence of such technology mandates produce pooling equilibria in which compliant and non-compliant developers are indistinguishable. The contribution is a political-economy account of retraining governance that treats information asymmetry as the central constraint on mandate credibility.