Bridging jurisdictions and legislatures: an LLM ensemble and arbiter framework for automated legal risk triage in digital media

Introduction: Autonomous and semi-automated AI tools are increasingly involved in digital-media workflows, creating a need for reliable preliminary screening of legal risks before publication or moderation decisions are made. Methods: This study presents an LLM Ensemble and Arbiter framework for cross-jurisdiction legal risk triage in digital media. The framework combines: (i) jurisdiction-specific legal grounding through a structured legal knowledge base (LawKB; 61 legal cards covering Ukrainian and EU norms), (ii) five heterogeneous open-weight LLMs, and (iii) a reproducible arbitration policy based on median risk aggregation and disagreement monitoring as a trigger for human review. The pipeline generates separate UA and EU risk assessments, a conservative global verdict, and a traceable rationales to support auditability. Results: The framework was evaluated on 288 news items with lawyer validation across 576 jurisdiction-specific records. In the global mode, the system achieved a mean absolute error of 0.86 and a confidence-weighted mean absolute error of 0.63, with an approval rate of 95.8 and 95.1% accuracy in selecting the best-performing model rationale, while limiting missed CRITICAL-level cases to 6.3%. Discussion: These results indicate that the proposed workflow can support conservative, inspectable, and human-supervised legal risk triage across two jurisdictions. Rather than replacing legal judgment, the framework functions as a preliminary decision-support mechanism that strengthens transparency, structured escalation, auditability, and procedural accountability in high-risk digital-media environments.