Traceability jurisdiction and accountability in distributed AI inference systems operating in stateless computational environments

Abstract

Abstract Contemporary AI systems increasingly rely on distributed inference environments involving cloud orchestration, external APIs, edge infrastructure, and dynamically allocated computational pathways. In many deployment models, inference activity no longer occurs within a stable technical environment or territorially coherent chain of execution. As a result, legally significant processing activity may become difficult to reconstruct across fragmented computational infrastructures operating under different jurisdictional conditions. This article examines how distributed inference architectures operating across ephemeral and organizationally fragmented execution environments complicate jurisdiction, attribution, and regulatory accountability. It uses “stateless” in the REST architectural sense identified by Fielding: each request is processed without reliance on server-side session state retained between requests. Statelessness does not itself eliminate traceability, because a stateless service can be comprehensively logged. The evidentiary problem instead arises when stateless request handling is combined with dynamic routing, short-lived infrastructure, fragmented logs, inconsistent retention, and divided organizational control. Adopting an interdisciplinary analytical approach, the article evaluates how that combination affects evidentiary reconstruction, operational visibility, and jurisdictional nexus. It proposes a traceability-oriented governance approach centered on inference provenance, interoperable logging, and jurisdictional anchoring, and develops an operational direction based on W3C provenance and distributed-tracing standards. The article argues that statelessness may aggravate the evidentiary problem in particular deployments but does not constitute it.