Evaluating International AI Governance: Balancing Technological Innovation in the Development of a Global Model for Generative AI
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Abstract
Generative Artificial Intelligence (GenAI) is advancing at an unprecedented pace, transforming industries, societies, and global technological landscapes. This thesis investigates the readiness of existing international AI governance frameworks to manage the projected growth of GenAI while safeguarding human security and fostering technological innovation. Adopting a governance and risk management lens, the thesis integrates perspectives from political science and business to evaluate how international and corporate frameworks align with the projected growth of generative AI. By combining quantitative market analysis with qualitative framework evaluation, the research identifies the trajectory of GenAI adoption through 2027, highlighting critical risks related to economic stability, privacy, and disinformation. Key frameworks, including the EU AI Act, UNESCO Recommendations, Gartner AI TRiSM, and others, are assessed for their effectiveness, transparency, enforcement, adaptability, and global applicability. Findings reveal that while these frameworks provide valuable guidance on ethical AI deployment and risk mitigation, they remain fragmented, unevenly enforced, and insufficiently flexible to keep pace with rapid innovation. Indicators such as cross-sector adoption, frequency of security breaches, and ongoing ethical debates are proposed to guide the development of future governance models. The thesis concludes by emphasizing the urgency of collaborative action among developers, policymakers, and users to establish adaptive, internationally aligned frameworks that balance innovation with human-centered safeguards. By serving as a foundational manuscript, this work provides a roadmap for designing a global AI governance model capable of addressing cross-cultural, sectoral, and technological complexities, ensuring the safe, equitable, and responsible integration of GenAI into society.
Description
A multidisciplinary thesis evaluating the readiness of global AI governance frameworks to manage the rapid growth of generative AI, integrating market forecasting and policy analysis to propose indicators for adaptive and human-centered regulation.