Computational Trust: Reframing Entity Authority as Annotation Efficiency in AI-Mediated Information Retrieval

This paper argues that algorithmic trust is not a reputation signal but a computational efficiency property: the measurable ease with which a system can evaluate new claims about an entity against its existing entity graph. Computational Trust is formalised as the inverse of marginal annotation cost — when new information fits the existing entity graph cheaply, the system processes it with high confidence and low latency; when it does not fit, the system hedges, re-evaluates, or rejects. The paper introduces the Entity Context Model (the cached entity-specific knowledge structure against which new claims are evaluated), the Annotation Cost Differential (the measurable expression of trust between competing entities), the Confidence Threshold (the binary gate separating low-trust from high-trust processing), and Corroboration Decay (the structural erosion of entity graph confidence through third-party content deletion). Grounded in practitioner evidence from 73 million brand profiles tracked since 2015 and the 2024 Google documentation disclosure confirming binary entity-URL attributes. Part of The Kalicube Process formal foundation.