符合局部控制，集体歧视：受监管金融中多智能体AI的治理架构

中文摘要 AI 辅助

金融机构开始在信贷、欺诈、催收、合规和运营控制中部署智能体工作流。治理在很大程度上仍以组件为中心：每个模型或智能体都在本地进行规范、测试、授权和监控。当机构风险源于许多局部可接受组件的联合行为时，这就不够了。我们将这一差距称为宪法性非组合性：局部合规检查不必组合成可接受的集体结果，如有限的不利影响、市场诚信或可追溯的责任。我们提出ARIA作为金融特定的参考架构和可证伪的研究议程，用于智能体群体治理。它在规范-问责、执行控制和保障-学习三个平面上组织了六项能力：政策规范、群体层面的观察与预期行为监控（M2）、有限权限、运行时遏制、自适应政策变更和保留的人类监督能力。两个模拟说明了局部控制下的共享信号薄文件排除，以及在构造的漂移机制中，观察与预期分布监控带来的更早预警。该贡献将这些控制映射到公平贷款、欧盟AI法案、模型风险和行为监管的证据需求，并以验证议程而非生产有效性声明作为结尾。

英文摘要

Financial institutions are beginning to deploy agentic workflows in credit, fraud, collections, compliance, and operational control. Governance remains largely component-centric: each model or agent is specified, tested, authorized, and monitored locally. That is insufficient when institutional risk arises from the joint behavior of many locally acceptable components. We call this gap constitutional non-compositionality: local compliance checks need not compose into acceptable collective outcomes such as bounded disparate impact, market integrity, or traceable accountability. We propose ARIA as a finance-specific reference architecture and falsifiable research agenda for agent-population governance. It organizes six capabilities across normative-accountability, execution-control, and assurance-learning planes: policy specification, population-level observed-versus-expected behavior monitoring (M2), bounded authority, runtime containment, adaptive policy change, and preserved human oversight competence. Two simulations illustrate shared-signal thin-file exclusion under local controls and earlier warning from observed-versus-expected distributional monitoring in a constructed drift regime. The contribution maps these controls to fair-lending, EU AI Act, model-risk, and conduct-supervision evidence needs, and closes with a validation agenda rather than a production-effectiveness claim.

发表机构

• Santander AI Lab（桑坦德人工智能实验室）
• Grupo Santander（桑坦德集团）

机构由 AI 辅助整理，请以论文原文为准。