Auditing the Algorithmic Leviathan: A Tiered Accountability and Reporting Standards Framework for Democratic Public Administration
TL;DR
A tiered accountability and reporting standards framework for decisions made under public authority is developed, which shifts standardization from AI system certification alone toward auditable institutional answerability, governance sustainability, and constitutional integrity.
Abstract
The accelerating use of algorithmic systems in public administration exposes a standardization gap between technical AI assurance and institutional accountability. This article develops a tiered accountability and reporting standards framework for decisions made under public authority. A qualitative design science method combines comparative institutional analysis, structured absence analysis, and standards architecture design. The empirical basis comprises international standards, the United States Department of Government Efficiency (DOGE)–Treasury access episode as an institutional control precursor, Australia’s Robodebt scheme as an automated-decision failure, and public sector governance arrangements in Estonia, Singapore, Japan, South Korea, Canada, and the United States. A replicable coding protocol traces documented accountability gaps to five auditable primitives: provenance tracking, decision logging, role attribution, contestability, and post-deployment audit. The primitives are organized into minimum, heightened, and systemic/constitutional tiers according to material influence, rights and essential service effects, civil service integrity, institutional independence, and substitutive capacity. The article also specifies a Public Sector Algorithmic Accountability Statement (PAAS), crosswalks its ten disclosure fields to GRI 1, GRI 2, and GRI 3, and demonstrates its operation through a fully worked hypothetical benefits eligibility application. A tier assignment decision aid, an assurance cycle, and a cost–feasibility model support implementation, including in small institutions. The framework’s novelty lies not in claiming new lifecycle controls, but in consolidating those controls around the public decision configuration, escalating them according to public authority consequences, and joining internal evidence to comparable public reporting. The proposal shifts standardization from AI system certification alone toward auditable institutional answerability, governance sustainability, and constitutional integrity.