AI Debris: Residual Risk and the Afterlife of Failed AI Systems

Abstract

AI governance frameworks primarily focus on risks during the development and deployment phases, implicitly treating system withdrawal as a technical shutdown. This paper argues that decommissioned AI systems generate residual risk, termed AI debris, that persists after model removal and continues to shape institutional behaviour, accountability, and trust. AI debris is defined as the post-withdrawal socio-technical residue of AI systems, including workflow dependency, data contamination, capability displacement (deskilling), legitimacy erosion, and accountability breakdown. The paper develops a typology of debris domains and identifies mechanisms through which debris persists, including institutional memory, path dependency, blame avoidance, and feedback effects in organisational data. To operationalise the concept, the paper proposes an evaluator-ready AI Debris Decommissioning Protocol (AIDP), a stepwise checklist specifying auditable evidence for freezing decision footprints, incident review, remediation, contestability, and post-withdrawal accountability assignment. A brief vignette of Amazon's discontinued hiring tool illustrates how algorithmic decision categories and screening heuristics can persist after system rollback. The paper contributes a practical governance instrument for regulators, auditors, and organisations seeking to prevent paper compliance, strengthen AI lifecycle governance, and improve institutional resilience in high-stakes decision environments.