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.
Artificial intelligence (AI) increasingly mediates leadership-relevant judgment through models, dashboards, metrics, decision-support systems, and autonomous agents. This conceptual article develops a socio-technical systems theory of systemically mediated leadership, defined as a nested system-level condition and recurrent process configuration through which human actors, AI systems, organizational routines, governance institutions, and affected stakeholders jointly produce and revise direction, meaning, consequential judgment, legitimacy, and accountability through recursive feedback. A problem-driven conceptual synthesis was updated through 3 August 2026. A structured discovery pass yielded 97 candidate records; 85 sources were retained after relevance screening, citation chaining, concept mapping, and comparison of eight candidate mechanism families. Four proposed qualification conditions jointly define the construct within the present framework: AI mediation, leadership relevance, distributed judgment, and recurrent institutional embedding. Five mechanism families explain transformations in responsibility, legitimacy, control, attention, and feedback timing: moral delegation, interpretive laundering, ceremonial oversight, metric-driven sensemaking, and ethical latency. A causal-loop model specifies justificatory reinforcement, capability atrophy, power insulation, and accountable correction. Their relative dominance produces three ideal-type dynamic regimes: accountable adaptation, stabilized trade-offs, and destructive drift. The theory predicts that organizations using equally accurate models may produce divergent leadership and accountability outcomes because their feedback, power, and oversight architectures differ. Responsible AI leadership thus depends on system architecture and contestable institutional practice, not leader intention, formal human approval, or model accuracy alone.
This paper extends the post-factual polity framework into AI infrastructure and public administration systems theory. It asks how proprietary analytical platforms alter the state’s capacity to produce, audit, and contest the categories through which risk, threat, eligibility, fraud, and deviance become actionable. Using a structured documentary case analysis of Palantir Technologies across United States agencies and allied jurisdictions, the study applies three diagnostic markers—categorical opacity, contestation displacement, and substitutive dependency—to examine the migration of sovereign classification into vendor-controlled infrastructure. The research gap was identified through an integrative review of public administration, AI governance, algorithmic accountability, systems theory, surveillance studies, and Palantir scholarship. The analysis distinguishes AI epistemic capture from ordinary IT vendor lock-in: the former concerns not merely technical dependence or high exit costs but the loss of public capacity to define and contest consequential administrative categories. The paper argues that administrative law, procurement reform, and algorithmic impact assessment remain necessary but insufficient when agencies lack substitutive capacity. It specifies untangling as a systems-level task involving capacity reconstruction, categorical repatriation, contractual restructuring, and procurement reorientation. Hybrid intelligence is advanced as a post-untangling architecture that embeds machine processing within contestable, accountable, and legally governed human judgment. The contribution is diagnostic, methodological, and design-oriented for AI systems governance.
Haris Alibašić· Syst.· 0 citations
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