Why Citation Data Cannot Explain AI Decision Exclusion

Abstract Recent analyses of large-scale citation behavior in conversational AI systems have suggested that visibility within citation graphs offers a proxy for influence and decision relevance. This article explains why that assumption fails in decision-adjacent contexts, and why reliance on citation data creates a false sense of governance control. Drawing on a multi-model evidentiary record of observed external AI behavior, we show that citation presence neither predicts nor explains decision eligibility, authority filtering, or entity substitution. Citation data describes a surface phenomenon. Decision exclusion occurs elsewhere, under different rules, and at later stages of model execution, leaving organisations unable to reconstruct or defend what influenced real decisions.