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Specialty referral pathways are a critical point at which healthcare inequities can emerge. Referral decisions may be shaped by insurance status, race, language, documentation practices, diagnosis severity, and the structure of available provider networks. Health systems often lack scalable and explainable tools for detecting inequitable referral patterns as they occur. As a result, discriminatory or structurally biased patterns may remain hidden within routine clinical operations. This article develops a conceptual explainable artificial intelligence model for identifying whether demographic or insurance factors unduly influence specialty referral decisions after accounting for clinical severity. The model is designed to support transparent, fairness-oriented referral analytics rather than replace clinical judgment. The proposed model uses a gradient-boosted classification framework trained on referral-eligible primary care encounters. Input features include patient demographics, insurance type, diagnosis severity, primary care note-derived complexity and completeness features, and provider network metrics, with SHAP-based explanation layers used for fairness auditing. Conceptually, the model could flag encounters in which predicted referral likelihood diverges from clinically expected patterns. These flags would be interpreted through explanation methods that attribute potential inequitable influence to insurance, demographic, documentation, or network-related factors. The model could help health systems audit, explain, and intervene on systemic specialty referral bias. Its central contribution is a transparent framework for moving referral equity work from retrospective description toward proactive, data-driven fairness review.