From Output to Path: Structural Conditions for Accountability in AI-Mediated Legal Systems

This paper addresses a recurring limitation in AI-mediated legal systems: outputs may appear coherent yet lack verifiable and reproducible processing paths. Empirical studies report substantial rates of error and unverifiable content in legal tasks, including documented instances of fabricated citations in formal submissions. These observations point to a structural issue rather than isolated failures. The paper reframes the problem as one of processing conditions rather than generative capability. Under conditions of informational abundance, the absence of structured filtration, routing, and transformation mechanisms prevents the formation of traceable paths linking input to output. As a result, outputs remain unassignable in terms of accountability, regardless of their surface plausibility. Drawing on a system-level view of institutional processes, the paper defines an internal processing layer composed of filter, routing, transformation, and closure. It further establishes three structural conditions for accountability—boundary, trace, and closure—under which outputs can be stabilized and responsibility can be meaningfully assigned. The structural grammar presented here also operates as a pedagogical framework. By rendering otherwise opaque processing paths into explicit sequences—filter, routing, transformation, and closure—it enables human operators to follow, reproduce, and audit institutional decision processes. In this sense, the framework is not limited to system design, but also functions as an instructional structure for reading rule-dense environments. The paper does not address pre-analytical admissibility conditions. Instead, it focuses on how imperfect inputs are processed within the system and whether such processing can produce auditable and reproducible paths. In this context, the Interface Routing Framework (IRF) is positioned as a minimal operational protocol that encodes processing paths into traceable, interface-level records without performing legal reasoning. The framework, together with related structural constructs introduced in this study, provides a unified representation for observing and comparing processing paths across institutional interfaces. The central claim is that accountability in AI-mediated legal systems does not depend on improving output accuracy alone, but on establishing structured, traceable processing paths. Without such conditions, outputs remain operationally opaque and cannot support reliable institutional use.