Artificial Intelligence, Governance, and Polity: A Comprehensive Literature Review

Abstract

Background: As public institutions embed Artificial Intelligence (AI) into administration, governing is shifting from conventional bureaucratic routines toward algorithm-driven decision systems. AI can enhance administrative effectiveness, support decision making and delivery of services, but also poses challenging questions around human rights, transparency, democratic accountability, and social fairness.

The review will consist of an overview of the multi-faceted landscape of AI governance, based on over 40 peer-reviewed studies and policy documents. It critically explores global regulatory approaches, the internal logic of policy design, barriers to implementation, standards for good regulation, policy use in public services and broader political-economic implications.

Key Findings:

Divergent regulation: Models differ significantly between jurisdictions from the EU's rights-based, tiered approach (EU AI Act/GDPR), to a piecemeal, sectoral model in the United States, a state-led programme in China, to a patchy style of regulation that is still developing in Asia-Pacific and Global South.

Weak structural integration: The challenge is not the absence of policy content, rather, the lack of structural integration, as evidenced by the lack of balance in the temporal planning horizon, coordination across government tiers and practical instruments of implementation.

Practical application of ethics: Using explainability, algorithmic fairness, and human oversight requires good architectures for risk management (e.g. NIST AI RMF), ongoing bias audits, privacy-preserving techniques like federated learning, and mechanisms to prevent regulatory capture.

While AI development is set to become a public-domain change, with three generations of automation, personalisation and prediction, if it is not designed and implemented inclusively with citizen involvement, it will create new digital divides and administrative opacity.

Implications: Polycentric governance arrangements involving multiple stakeholders are required to realize effective governance of AI. Future policy should focus on developing institutional capacity, on creating coherence across policy levels and on creating safeguards for vulnerable groups based on evidence, in such a way that AI deployment is compliant with democratic values, public values and human dignity.