Computable Gap Assessment of Artificial Intelligence Governance in Children's Centres: Evidence-Mechanism-Governance-Indicator Modelling of UNICEF's Guidance on AI and Children 3.0 Based on the Graph-GAP Framework

This paper addresses the practical challenges in governing “child-centred artificial intelligence”: regulatory texts often outline principles and requirements yet lack reproducible evidence anchors, clear causal pathways, executable governance toolchains, and computable audit metrics. To bridge this gap, this paper proposes the Graph-GAP methodology: decomposing requirements from authoritative policy texts into a four-layer graph structure of ‘evidence-mechanism-governance-indicator’, and constructing dual quantifiable metrics—GAP scoring and ‘mitigation readiness’—to identify governance gaps and prioritise actions. Using UNICEF Innocenti's Guidance on AI and Children 3.0 as primary material, this paper provides reproducible data extraction units, coding manuals, graph patterns, scoring scales, and consistency verification protocols. It further offers exemplar gap profiles and governance priority matrices for ten requirements. Findings indicate that compared to privacy and data protection, themes such as ‘child well-being/development,’ ‘explainability and accountability,’ and ‘cross-agency implementation and resource allocation’ are more prone to indicator gaps and mechanism gaps. Priority should be given to translating regulatory requirements into auditable governance through closed-loop systems incorporating child rights impact assessments, continuous monitoring metrics, and grievance redress procedures. At the coding level, this paper further proposes a ‘multi-algorithm review-aggregation-revision’ mechanism: deploying rule encoders, statistical/ machine learning evaluators and large-model evaluators with diverse prompt configurations as parallel coders. Each extraction unit yields E/M/G/K and Readiness scores alongside evidence anchors. Consistency, stability, and uncertainty are validated using Krippendorff’s α, weighted κ, ICC, and bootstrap confidence intervals. The scoring system is operational and auditable.