Jurisdictional Displacement in Automated Decision Systems: Two Retrospective Case Studies

Relationship to prior work This paper is a companion empirical note to The Inheritor Problem: Diagnosing Sovereign Transition Beyond the Judgement Boundary (Zenodo DOI: https://doi.org/10.5281/zenodo.18267800). Whereas the Inheritor paper defines the theoretical failure mode and irreversibility boundary, the present document provides retrospective case studies demonstrating the same Phase-2 jurisdictional displacement pattern across additional domains. This paper does not introduce new theory; it operationalizes and stress-tests the existing framework through comparative analysis. This publication presents two retrospective case studies examining jurisdictional displacement in automated decision systems: automated credit decisioning and automated hiring / applicant tracking systems (ATS). Each case analyzes a documented transition from assistive, diagnostic use of algorithmic tools toward determinative or sovereign execution, focusing on a three-phase pattern observed in multiple domains: Human-centered discretion with algorithmic support Transitional co-movement marked by declining human override pathways and inflation of model-grounded explanations System-level enforcement where human judgment is structurally subordinated The case studies are constructed exclusively from observable artifacts such as regulatory disclosures, explanation templates, rejection notices, policy documentation, and public complaint records. No internal system access or proprietary data is assumed. These analyses are intended to test the cross-domain applicability of diagnostic indicators proposed in prior work on jurisdictional displacement and judgment boundary erosion. The publication makes no claims of universality, prevention, or operational completeness, and does not propose thresholds or enforcement mechanisms. The purpose of this work is empirical cartography: to document a repeatable transition pattern across independent domains, and to establish a falsifiable basis for further investigation into early-warning diagnostics for loss of human standing in automated decision environments.