Constitutional Intelligence as a Governance Architecture for Trustworthy Public-Sector AI

Human-centric AI governance in the European Union. Accountability, fundamental rights, and institutional resilience in public administration

Artificial intelligence is increasingly used in public administration to classify individuals, assess risks, prioritize cases, support eligibility determinations and guide the allocation of public resources. In the European Union, these uses are governed by the Artificial Intelligence Act, the GDPR and the Charter of Fundamental Rights. Formal compliance, however, does not by itself ensure lawful and accountable administration. AI relocates discretion from the visible act of decision-making to less visible choices concerning data, model design, procurement, thresholds and interface architecture. This Policy and Practice Review therefore treats human-centric AI governance not as a general ethical aspiration, but as an administrative and constitutional framework for governing public power. Drawing on EU law, public administration scholarship and a comparative institutional analysis of selected Member State practices, it develops six interdependent dimensions: legal anchoring, accountable discretion, fundamental rights by design, meaningful human oversight, contestability and justification, and institutional resilience. The analysis shows that common EU rules may produce unequal levels of protection where public authorities differ in technical expertise, audit capacity, procurement independence and access to effective remedies. It also argues that accountability must follow the chain of influence through which algorithmic systems shape administrative outcomes, rather than only the formal chain of decision-making. The article translates this framework into actor-specific recommendations concerning fundamental rights impact assessments, procurement, auditability, human oversight, transparency, contestability and post-deployment monitoring. It concludes that AI-enabled administration remains legitimate only where public authorities retain the capacity to understand, justify, correct, suspend and democratically control the systems they use.

AI for citizens—empowering public institutions worldwide through inclusive, transparent and ethical AI

Auditing as a Governance Mechanism for Artificial Intelligence: Institutional Design, Accountability, and Ethical Oversight

The rapid diffusion of artificial intelligence (AI) across organisational and societal settings has heightened concerns about accountability, transparency, and ethical oversight. Existing governance mechanisms, including regulation and principle-based ethics frameworks, often struggle to address the scale, opacity, and socio-technical complexity of AI systems. In response, auditing has increasingly been proposed as a means of implementing accountability by translating ethical and legal expectations into structured oversight practices. The study employs a structured literature review methodology, analysing 71 peer-reviewed articles published between 2020 and 2025, retrieved from Scopus, Web of Science, and ProQuest. Through thematic synthesis, the review shows that AI auditing has evolved beyond technical verification towards a socio-technical governance infrastructure grounded in transparency, independence, ethics integration, and professionalisation. However, its effectiveness is constrained by persistent challenges, including algorithmic opacity, regulatory lag, fragmented standards, capability gaps, and risks of symbolic compliance. The study positions auditing as both a central tool and a critical institutional challenge within AI governance, offering insights for scholars, regulators, and practitioners seeking durable accountability mechanisms for responsible AI.

Artificial Intelligence in Public Administration in the U.S.: Lessons and Policy Implications for the Republic of Armenia

Artificial intelligence (AI) is rapidly reshaping public administration worldwide. In the United States, AI-enabled systems support automation, predictive analytics, fraud detection, and data-driven policymaking while simultaneously raising ethical concerns related to fairness, transparency, privacy, accountability, and discrimination. This article analyzes U.S. federal frameworks for ethical AI governance, including the Blueprint for an AI Bill of Rights, the NIST (National Institute of Standards and Technology) AI Risk Management Framework, and recent Executive Orders. Their strengths, limitations, and institutional implications are assessed in comparison with global approaches. Building on this review, the article proposes a context-adapted ethical AI governance model for the Republic of Armenia. Key recommendations include the adoption of a national AI strategy, establishment of oversight institutions, improvement of data-quality systems, strengthening procurement integrity, and embedding AI ethics in civil service reforms. The paper concludes that Armenia can leverage U.S. and global best practices to develop a transparent, accountable, and citizen-centered AI ecosystem.

Collaborative Governance and Algorithmic Accountability: Regulating Risks and Restructuring Organizations for AI Integration

A normative analysis of thirteen recent studies on the challenges of technology implementation, ethical trust, and legal regulation suggests that the current governance dilemma stems not only from technological limitations but also from institutional neglect, which enables accountability avoidance.

Artificial Intelligence Adoption in Government Institutions: Ethical Governance, Public Trust, and Risk Management

The application of artificial intelligence (AI) technologies in the public sector has led to improved public services, enhanced administrative performance, and strengthened automated decision-making. However, the increasing reliance on AI systems has raised concerns regarding accountability, ethical compliance, privacy protection, transparency, human oversight, and risk management. This study, employing both conceptual and qualitative research methodologies, examines the governance factors and requirements for responsible AI implementation in the public sector. The research methodology includes a comparative analysis of international AI governance frameworks and regulations. The study identifies key dimensions influencing responsible AI implementation, such as accountability, human oversight, ethical governance, legal compliance, risk management, and transparency. The findings demonstrate that the adoption of responsible AI cannot be achieved through technological means alone but also requires a commitment to comprehensive governance mechanisms. Furthermore, the sequential interaction and interdependence of governance factors reduce operational and societal risks, increase transparency and explainability, and foster public trust in the systems. This study contributes to enriching the culture and knowledge of AI governance, and the proposed framework helps government sector leaders develop responsible AI governance in accordance with international standards and regulations.