Dr. Haris Alibašić

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.

Automated license plate readers (ALPRs) are often evaluated as discrete police tools, although their public power arises from cross-vendor socio-technical infrastructure. This article examines roadside and mobile sensors, vehicle-attribute classification, cloud archives, commercial databases, real-time crime center integration, interagency access, automated alerts, and police action. Flock Safety supplies the principal documentary case because unusually extensive public records permit system-level tracing; Axon/Fusus, Motorola Vigilant/VehicleManager, and federal access to commercial ALPR data establish the wider vendor-independent boundary. A structured documentary analysis of 59 sources triangulates official records, peer-reviewed research, vendor materials used only for stated functions, and record-based investigations. It integrates boundary critique, control-structure mapping, feedback analysis, constitutional doctrine, a STRIDE-informed threat model, and empirical research on policing effectiveness and surveillance effects through 3 August 2026. The analysis identifies four conditional mechanisms: infrastructure aggregation, authority diffusion, asymmetric feedback, and rights invisibility. The article reformulates the Rights Control Deficit (RCD) as a non-arithmetic profile relation between operational demands and effective governance capacity and applies it to three documented configurations and a clearly labeled normative benchmark. Seven falsifiable propositions specify variables, indicators, suitable methods, and disconfirming conditions for later empirical study. A rights-preserving hybrid-intelligence architecture combines bounded automation with judicial authorization, short retention, sensitive-location protections, immutable audit, availability safeguards, independent review, contestability, sanctions, and credible termination authority. The evidence identifies capabilities, activated pathways, and conditional risks; it does not estimate population prevalence or a universal ALPR-specific causal effect. Meaningful human oversight is an institutional control property, not merely an officer’s presence at an interface.

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.

Public-sector sustainability and climate reporting increasingly address environmental exposure, governance, and financial effects, yet existing frameworks do not adequately disclose preparedness for low-probability, high-impact systemic risks whose probabilities, timing, thresholds, and transmission channels remain deeply uncertain. This article develops a Public-Sector Resilience Reporting Standard (PSRRS) as a pre-standard architecture for government preparedness disclosure. The design has three bounded objectives: diagnose cross-framework disclosure gaps, translate these gaps into a theoretically grounded capability-to-disclosure architecture, and demonstrate its analytical use through an illustrative Florida application and two hazard-neutral stress tests. The documentary corpus includes international sustainability and public-sector reporting standards, ISO and UNDRR resilience and continuity instruments, three Florida resilience documents, and peer-reviewed literature on resilience governance, decision-making under deep uncertainty, critical infrastructure interdependency, catastrophic uncertainty, climate-risk disclosure, public finance, climate-risk pricing, local-government credit risk, investor attention, and ransomware service disruption. A structured interpretive coding protocol classifies each framework as explicit, partial, or not explicit across nine disclosure dimensions; a codebook appendix identifies the assessment criteria, the a priori and inductively refined dimensions, and the validation boundaries. Florida is not treated as a basis for statistical or jurisdictional generalization. Instead, it illustrates how a comparatively developed resilience architecture may disclose statutory continuity, critical-asset data, project ranking, and output metrics while leaving systemic dependencies, adaptive triggers, long-horizon fiscal exposure, residual service risk, distributional effects, and assurance mechanisms insufficiently visible in the reviewed reporting corpus. AMOC and case-grounded cyber-fiscal stress tests show how the PSRRS shifts reporting from hazard inventories and funded projects toward auditable evidence of institutional capacity, adaptive readiness, and public-value protection. The article specifies mandatory, recommended, and optional clauses, evidence requirements, indicator examples, a disclosure index, a sample report structure, and a three-tier pilot conformity model. The contribution is conceptual and operational, but not yet a validated formal standard; cross-jurisdictional piloting, inter-rater coding, cost testing, assurance testing, and stakeholder consultation are identified as the next stage of standardization.

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.

The sustainable development paradigm, as institutionalized through the United Nations Sustainable Development Goals (SDGs), has reached a state of structural exhaustion. Only 17% of SDG targets were on track at the 2024 midpoint, with over one-third stalled or regressing. This conceptual paper argues that sustainable development is dead as a governance paradigm, killed by four convergent forces: political delegitimation processes by populist-authoritarian movements, capture of UN sustainability mechanisms by financialized market instruments, conceptual stretching beyond analytical coherence, and coordination failure across global, national, and local governance scales. Drawing on the Quadruple Bottom Line (QBL) framework, the post-factual polity thesis, and adjacent literatures on resilience-based governance, paradox theory, and sustainability transitions, the paper proposes adaptive transformation governance (ATG) as a successor framework that embeds governance accountability as a structural design principle.

The rapid integration of artificial intelligence into private and public-sector decision-making has outpaced the development of standards governing the interaction between human judgment and machine intelligence. Existing frameworks—the EU AI Act Regulation, the NIST AI Risk Management Framework, and ISO/IEC 42001—regulate AI systems as discrete technical artifacts but do not standardize the hybrid intelligence configurations in which human cognition and algorithmic outputs jointly produce governance decisions. This paper proposes a three-layer standards framework comprising technical interoperability standards governing how AI outputs are communicated to human decision-makers, procedural standards governing human-AI task allocation and escalation protocols, and accountability standards governing responsibility attribution in distributed decision configurations. The framework is grounded in the Quadruple Bottom Line (QBL), which adds governance as a fourth sustainability dimension. To move beyond a purely conceptual contribution, the paper provides operationalization tools—including a role allocation matrix, confidence calibration thresholds, an accountability mapping template, and a domain classification schema—and proposes a three-tier conformity assessment methodology for evaluating framework implementation. By establishing the hybrid human–AI decision configuration as the unit of standardization, the paper introduces a governance architecture that enables operational, auditable, and comparable hybrid intelligence systems.

Artificial intelligence systems increasingly shape not only what information citizens access but the interpretive frameworks through which they perceive public affairs, posing a governance challenge that public administration scholarship has yet to theorise. Through conceptual and normative analysis, the paper develops the construct of perceptual infrastructure – the cognitive and informational substrate of democratic deliberation – drawing on information theory, public administration and regulatory-governance scholarship, and tests it against the EU regulatory architecture. The analysis shows that the algorithmic construction of perceptual frameworks constitutes a distinct governance domain that the EU AI Act and the Digital Services Act do not reach, owing primarily to a regulatory omission rather than an implementation deficit: existing provisions take systems, use cases and identifiable harms as their object, not the cumulative, longitudinal construction of perception. An accountability framework is proposed – incorporating aggregate transparency, perceptual sovereignty as a citizen right, proportional responsibility and meta-perceptual literacy – with concrete implications for administrative accountability, regulatory capacity and democratic resilience.

Impact - This article offers critical insights for policy-makers, development strategists, environmental advocates, and academics engaged in the rigorous pursuit of sustainable development within the context of political uncertainties and evolving global challenges. It amplifies the discourse on integrating and reporting the United Nations Sustainable Development Goals (SDGs) into national development strategies, using Kosovo's endeavours as a pivotal case study. By emphasizing the role of resilience theory in enhancing adaptive governance and multi-sectoral collaboration for SDG achievement, this article substantiates the imperative for innovative, inclusive, and resilient approaches to SDG integration and reporting in national development planning. The findings and recommendations advocate for the strategic alignment of national development initiatives with global sustainability objectives, underscoring the importance of establishing environmental sustainability as a core pillar and the necessity for comprehensive stakeholder engagement in the policy-making process. As such, this work contributes to the broadening evidence base supporting the integration of resilience and sustainability principles into the governance and implementation of development strategies.

This article delves into integrating and reporting the United Nations Sustainable Development Goals (SDGs) within the Republic of Kosovo's National Development Strategy (NDS), emphasizing the pivotal role of resilience and adaptability in governance, social inclusivity, and economic growth. Through qualitative analysis, gaps are identified, and strategic recommendations are made to enhance SDG integration in Kosovo's development agenda. Utilizing resilience theory as an analytical tool, the article dichotomizes the interplay between reporting mechanisms, national strategies, and global sustainability benchmarks to bolster the integration of SDGs within national planning documents.

The integration of artificial intelligence and human decision-making within blockchain systems has raised complex ethical considerations, necessitating the development of comprehensive theoretical frameworks. This research develops a multi-paradigm ethical framework addressing the ethical dimensions of hybrid intelligence—the dynamic interplay between human judgment and artificial intelligence—in the governance of blockchain technology and cryptocurrency systems. Drawing upon complexity theory and institutional theory, this study employs a theory synthesis methodology to investigate inherent paradoxes within hybrid intelligence systems, including how transparency creates new opacities in AI decision-making, decentralization enables centralized control, and algorithmic efficiency undermines ethical sensitivity. Through PRISMA-compliant systematic literature analysis of 50 relevant publications and theoretical synthesis, this research demonstrates how blockchain technology fundamentally redefines hybrid intelligence by establishing novel forms of trust, accountability, and collective decision-making. The framework advances three testable propositions regarding emergent intelligence properties, adaptive capacity, and institutional legitimacy while providing practical governance principles and implementation methodologies for blockchain developers, regulators, and participants. This study contributes theoretically by bridging the fields of complex systems and institutional analysis, integrating complex adaptive systems with institutional legitimacy processes through a multi-paradigm integration methodology. It delivers an ethical framework that addresses accountability distribution in Decentralized Autonomous Organizations, quantifies ethical challenges across major platforms, and offers empirically validated guidelines for balancing algorithmic autonomy with human oversight in decentralized systems.