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Clinical triage algorithms increasingly influence access to emergency care, specialty referral, admission, and follow-up. As patient populations and clinical practice patterns change, these systems can silently drift toward biased performance. Current fairness assessments are often retrospective, episodic, and disconnected from operational triage workflows. They may identify inequity after harm has already accumulated rather than detecting emerging bias as it develops. This article proposes an explainable AI model for continuous monitoring of algorithmic bias in clinical triage systems. The model focuses on demographic drift, outcome disparities, prediction confidence, and referral decision patterns as complementary bias signals. The proposed model uses operational triage logs, demographic distributions, prediction outputs, outcome indicators, and referral decisions to generate a conceptual fairness risk signal. SHAP-based explanation modules decompose the signal into interpretable contributors for clinical governance teams. Conceptually, the model would detect divergence in referral patterns across demographic groups, identify whether the divergence coincides with demographic drift, flag subgroup-specific prediction confidence concerns, and explain the likely drivers of the alert. The output would support timely review rather than automated punitive action. The model could transform algorithmic fairness from a periodic retrospective report into a continuous, transparent, and operationally actionable surveillance system. It is designed as a governance-oriented framework rather than an experimental performance claim.