Abstract

As artificial intelligence (AI) systems become deeply embedded in economic, social, and civic domains, societies face the challenge of ensuring that human–AI coexistence occurs in ways that are ethical, equitable, responsible, and sustainable. Governance frameworks and policy models provide structured approaches for regulating AI technologies, balancing innovation with public interest, and addressing risks such as discrimination, loss of privacy, economic disruption, and erosion of democratic processes. This paper synthesizes multidisciplinary perspectives on AI governance, surveying institutional frameworks, regulatory mechanisms, standards, and normative models from international bodies, governments, and research communities. We examine core governance principles—human rights, accountability, transparency, fairness, safety, and sustainability—and explore how they are operationalized through policies, standards, and institutional arrangements. We present a systematic research methodology for analyzing and designing governance frameworks, including stakeholder analysis, risk assessment, normative mapping, and iterative policy evaluation. The paper discusses the advantages and limitations of current governance models, drawing on case studies in healthcare, autonomous weapons, labor markets, and data ecosystems. Through comparative analysis, we highlight emergent trends such as rights‑based regulation, adaptive risk‑based approaches, and multistakeholder governance. The conclusion identifies key gaps and proposes future directions for harmonizing AI policy globally while respecting cultural, legal, and economic diversity.