Abstract

Artificial intelligence (AI) is revolutionizing how humans conduct transactions, make decisions, and engage in social settings, with applications in important sectors, such as healthcare, banking, and criminal justice. While AI systems offer full efficiency, they additionally pose significant threats and ethical issues, such as accountability gaps and algorithmic biases. The challenge for AI-driven governments is to establish governance structures that effectively manage the evolving threats posed by increasingly complex and autonomous AI systems. This paper seeks to establish a theoretical framework for analyzing AI governance by examining mainly four critical issues: bias, transparency, economic influence, and the need for adaptive regulatory approaches. These issues are selected based on their relevance to the societal and ethical implications of AI, their impact on public trust, and their influence on fairness and equitable outcomes. Through this lens, the paper evaluates current regulatory models and self-regulation initiatives, analyzing their capacity to balance effective oversight with the need for innovation. Drawing on governance theories and socio-technical systems, the paper explores how these frameworks can address AI’s risks while enhancing its positive contributions. In addition, the paper identifies key gaps in existing governance approaches and calls for further research and policy development. Ultimately, this work aims to advance the understanding of AI governance by offering actionable insights that inform the creation of models capable of protecting public interests while enabling technological progress.