Algorithmic Triage in Administrative Complaint Systems

Abstract

Background: The practical operation of policy relating to algorithmic triage differs across settings, suggesting a need to examine implementation capacity and institutional design directly.

Objective: This study sought to explain differences in the implementation of algorithmic triage by examining institutional responsibility, administrative capacity, and complaints.

Methods: A comparative analysis was undertaken across 5 jurisdictions or institutional settings. Primary legal and policy documents were integrated with structured organizational evidence and, where relevant, key-informant material. Cases were assessed on authority, coordination, transparency, safeguards, enforcement, and implementation capacity.

Results: Three distinct approaches to algorithmic triage were evident across the cases. Settings that combined clear mandates, formal coordination, and review mechanisms achieved a 25% higher composite implementation score; fragmented responsibility was the common feature in cases experiencing delay or inconsistent enforcement.

Conclusion: The findings underline a practical gap between formal policy on algorithmic triage and the institutional work required to deliver it consistently. This means that AI governance in the public sector needs enforceable controls, capable staff, documented accountability, and meaningful human review.

Study design: case-flow observational study.

Publication history: Originally published in print on 15 June 2026, Volume 6, Article 15. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.