Artificial Intelligence Governance of Emergency Department Triage Acuity Models Under Unmeasured Nurse Union Staffing Grievance Escalation: A Political Fairness Evaluation

Emergency department triage acuity models increasingly rely on artificial intelligence to rank patients, yet their governance assumes that the main threat to fairness is measurement error in clinical variables. A more consequential threat is political: nurse union staffing grievance escalation is an unmeasured institutional signal that shapes how triage scores are produced, contested and overridden, and it is systematically absent from the data on which fairness audits depend. Because no observational data are available, the framework is operationalised as a before-after-control-impact design and its operating characteristics are characterised under a declared variance structure with an intraclass correlation of 0.64, between-cluster SD of 6.0 and residual SD of 4.5. 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; 54 clusters are required for 80% power at a 5 percentage point effect. The contribution is a governance argument that fairness evaluation must model the political production of triage data, not only its statistical properties.