用多智能体系统提升数据跨境合规检查准确率
Multi-Agent Legal Verifier Systems for Data Transfer Planning
- 拆分法律解读、业务评估、风险分析为三个专业智能体协同工作
- 整体准确率达72%，清晰合规案例准确率90%，比单智能体高21个百分点
- 适合需要可解释合规验证的AI系统开发者和监管科技从业者
在严格的隐私法规（如日本《个人信息保护法》（APPI））背景下，人工智能驱动的数据传输规划中的法律合规性日益重要。我们提出一种多智能体法律验证系统，将合规检查分解为专门负责法令解释、业务情境评估和风险分析的智能体，并通过结构化合成协议进行协调。在包含200个经修订的APPI第16条案例的分层数据集上进行评估，该系统达到72%的准确率，比单智能体基线高出21个百分点；在清晰合规案例中准确率达90%（基线仅16%），同时保持对明显违规行为的完美检测。尽管在模糊场景中仍存挑战，结果表明领域专业化与协同推理能显著提升法律AI性能，为可信且可解释的自动化合规验证提供可扩展、法规敏感的框架。
原文摘要 · Abstract (English)
Legal compliance in AI-driven data transfer planning is becoming increasingly critical under stringent privacy regulations such as the Japanese Act on the Protection of Personal Information (APPI). We propose a multi-agent legal verifier that decomposes compliance checking into specialized agents for statutory interpretation, business context evaluation, and risk assessment, coordinated through a structured synthesis protocol. Evaluated on a stratified dataset of 200 Amended APPI Article 16 cases with clearly defined ground truth labels and multiple performance metrics, the system achieves 72% accuracy, which is 21 percentage points higher than a single-agent baseline, including 90% accuracy on clear compliance cases (vs. 16% for the baseline) while maintaining perfect detection of clear violations. While challenges remain in ambiguous scenarios, these results show that domain specialization and coordinated reasoning can meaningfully improve legal AI performance, providing a scalable and regulation-aware framework for trustworthy and interpretable automated compliance verification.
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