Navigating governance paradigms: A cross-regional comparative study of generative AI governance processes & principle

• Main Authors: LUNA, Jose, TAN, Ivan, XIE, Xiaofei, JIANG, Lingxiao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Generative artificial intelligence governance; GenAI governance; Artificial Intelligence and Robotics; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9536; https://ink.library.smu.edu.sg/context/sis_research/article/10536/viewcontent/2408.16771v1.pdf
• Institution: Singapore Management University

As Generative Artificial Intelligence (GenAI) technologies evolve at an unprecedented rate, global governance approaches struggle to keep pace with the technology, highlighting a critical issue in the governance adaptation of significant challenges. Depicting the nuances of nascent and diverse governance approaches based on risks, rules, outcomes, principles, or a mix across different regions around the globe is fundamental to discern discrepancies and convergences and to shed light on specific limitations that need to be addressed, thereby facilitating the safe and trustworthy adoption of GenAI. In response to the need and the evolving nature of GenAI, this paper seeks to provide a collective view of different governance approaches around the world. Our research introduces a Harmonized GenAI Framework, "H-GenAIGF," based on the current governance approaches of six regions: European Union (EU), United States (US), China (CN), Canada (CA), United Kingdom (UK), and Singapore (SG). We have identified four constituents, fifteen processes, twenty-five sub-processes, and nine principles that aid the governance of GenAI, thus providing a comprehensive perspective on the current state of GenAI governance. In addition, we present a comparative analysis to facilitate the identification of common ground and distinctions based on the coverage of the processes by each region. The results show that risk-based approaches allow for better coverage of the processes, followed by mixed approaches. Other approaches lag behind, covering less than 50% of the processes. Most prominently, the analysis demonstrates that among the regions, only one process aligns across all approaches, highlighting the lack of consistent and executable provisions. Moreover, our case study on ChatGPT reveals process coverage deficiency, showing that harmonization of approaches is necessary to find alignment for GenAI governance.