自主AI系统中的问责不对称性与结构性信任
Accountability Asymmetry and Structural Trust in Autonomous AI Systems
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中文总结 AI 辅助
本文针对自主AI系统中问责不对称导致的信任问题，提出将其治理视为基础设施可靠性问题，通过工程异质性方案，即独立监测审查补充流程，解决该问题。
中文摘要 AI 辅助
自主AI系统（如AI智能体）正被越来越多地委托执行科学计算基础设施中的操作工作，其任务可能从准备输入或路由警报开始，扩展到更改配置或提交作业。这种委托产生了实际的信任问题，因为让我们信任人类操作员的制度逻辑无法转移到基于优化的系统上。错误决策可能会严重损害人类操作员的未来，而AI系统虽受工程控制，但在该制度意义上不承担后果，我将这种不匹配称为问责不对称性。问题不仅在于模型无法像人一样被惩罚，更深层的是后果会落在对系统负责的人和机构身上，而非选择行动的组件。对齐可改善模型行为，责任可约束组织，但两者都无法产生约束人类操作员的事前威慑。因此，本文将自主AI治理视为基础设施可靠性问题，提出的建设性方案是工程异质性：提议行动的流程不应是其唯一的批准者和审计者，随时间推移的独立监测与审查为该流程提供额外检查。
英文摘要
Autonomous AI systems (such as AI agents) are increasingly being delegated operational work across scientific-computing infrastructure. Their assignments may begin with preparing an input or routing an alert and extend to changing a configuration or submitting a job. That delegation creates a practical trust problem because the institutional logic that lets us trust human operators does not transfer to optimization-based systems. A bad decision can damage a human operator's future, sometimes severely. An AI system remains subject to engineering control, but it does not bear consequences in that institutional sense. I use the term accountability asymmetry for this mismatch. The issue is not simply that a model cannot be punished as a person can. The deeper problem is that consequence lands on the people and institutions responsible for the system rather than on the component selecting the action. Alignment can improve model behavior, and liability can discipline the organization, but neither creates the same pre-action deterrent that governs a human operator. This paper therefore treats autonomous AI governance as a problem of infrastructure reliability. Its constructive proposal is engineered heterogeneity: the process that proposes an action should not serve as its sole approver and auditor. Independent monitoring and review over time provide additional checks on that process.
