Accountability Asymmetry and Structural Trust in Autonomous AI Systems

Abstract

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.