A Systematic Review of Strategic Artificial Intelligence Governance Frameworks and Mechanisms in Studies from 2017-2025
Pages 9-54
https://doi.org/10.22083/cssms.2025.538964.1067
Hassan Bashir, Mohammadjavad Ravi
Abstract Introducion: Artificial intelligence, as one of the contemporary disruptive technologies, has transformed the structures of societies and industries. However, the rapid proliferation of this technology has raised questions about its management and oversight. AI governance, beyond technical aspects, requires attention to ethical, social, and legal dimensions in order to prevent negative consequences such as bias, privacy breaches, or discrimination. The swift advancement of artificial intelligence has challenged traditional regulatory frameworks that were designed for information and communication technologies. The speed of algorithm development has outpaced legislators’ ability to enact up-to-date regulations, bringing challenges such as algorithmic bias, privacy violations, and potential misuse. Among the foundational and influential theories in this domain are Algorithmic Governance/Regulation and the artificial intelligence ecosystem theory. The lack of global consensus on definitions and approaches, coupled with policy fragmentation, has made the formulation of effective frameworks difficult.
Methods: Using a systematic protocol, we searched major databases (2017–2025) for “AI governance,” retrieving 130 records. After de-duplication, screening, and criterion-based appraisal (English; direct governance focus; excluding first-generation symbolic AI), 63 studies remained. We extracted bibliographic/methodological data and qualitative evidence on ethics, policy, regulation, and operationalisation. Iterative thematic analysis-constant comparison, memoing, open-to-axial coding, return-to-text checks, and audit trails-ultimately produced 297 initial codes, consolidated into 16 organising codes and four overarching themes.
Results: Findings converge on four areas. (1) Ethics: prioritising transparency, explainability, bias mitigation, and meaningful human oversight to sustain trust. (2) Policies and outlooks: countries pursue divergent strategies-EU law-centric, US trust/ethics-led, and China industry-plus-social-stability-yet share goals to balance innovation with safety. (3) Coordination: persistent gaps remain in common definitions, interoperable standards, data-sharing mechanisms, and dispute-resolution, exacerbated by geopolitical frictions. (4) Governance domains: interlocking technical, ethical, and legal layers operate across national, sectoral, and organisational levels and throughout the system lifecycle, operationalised through risk classification, independent audits, model/data cards, incident reporting, procurement clauses, and other enforceable controls that translate principles into practice..
Discussion: Grounded in the review’s evidence, we propose a “multi-layered governance based on risk and trust” model anchored in four pillars-ethics, policy, governance layers, and application. It operationalises values as measurable, enforceable controls; applies proportionately across technical, ethical, and legal layers; spans national, sectoral, organisational levels and the lifecycle. Practical levers include risk classification, independent audits, red-teaming, model/data cards, incident reporting, procurement clauses, and rollback protocols. The model reduces principle-to-practice gaps, improves accountability and interoperability, and preserves innovation under safety floors.

