[Paper Review] Navigating Governance Paradigms: A Cross-Regional Comparative Study of Generative AI Governance Processes & Principles

José Diogo Forte de Oliveira Luna, Ivan Tan+2 more|arXiv (Cornell University)|Aug 14, 2024

Ethics and Social Impacts of AI4 citations

TL;DR

This paper proposes the Harmonized GenAI Framework (H-GenAIGF), a cross-regional comparative analysis of generative AI governance across six regions—EU, US, China, Canada, UK, and Singapore—identifying 15 processes, 25 sub-processes, and nine principles. It reveals that risk-based approaches cover the most processes, while only one process is universally aligned, underscoring the urgent need for harmonization to ensure trustworthy GenAI deployment.

ABSTRACT

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.

Motivation & Objective

• To analyze and compare governance approaches for generative AI across six major global regions.
• To identify commonalities and disparities in governance processes and principles across regions.
• To develop a unified framework—H-GenAIGF—that synthesizes current governance practices into a harmonized model.
• To assess the coverage of governance processes across different regional paradigms and identify critical gaps.
• To evaluate the real-world applicability of current governance models using a case study on ChatGPT.

Proposed method

• The study identifies four constituents, 15 core processes, 25 sub-processes, and nine principles from existing governance frameworks across six regions.
• A comparative analysis is conducted to assess the extent of process coverage across regional governance models.
• The framework H-GenAIGF is constructed by synthesizing overlapping and distinct elements from regional approaches.
• Governance paradigms are classified as risk-based, principle-based, rule-based, outcome-based, or mixed, with coverage measured per paradigm.
• A case study on ChatGPT evaluates the practical alignment of governance processes in a real-world GenAI system.
• Statistical comparison of process coverage across paradigms is used to rank effectiveness and identify deficiencies.

Experimental results

Research questions

• RQ1 How do different regional governance paradigms for generative AI compare in terms of process coverage?
• RQ2 Which governance approach—risk-based, mixed, or principle-based—provides the broadest coverage of essential GenAI governance processes?
• RQ3 To what extent is there alignment across regions in the implementation of core governance processes?
• RQ4 What are the key gaps in current governance frameworks when applied to real-world GenAI systems like ChatGPT?
• RQ5 How can a harmonized framework improve the consistency and trustworthiness of global GenAI governance?

Key findings

• Risk-based governance approaches cover the highest proportion of the 15 core processes, followed by mixed approaches.
• Principle-based and rule-based approaches cover less than 50% of the processes, indicating significant coverage gaps.
• Only one governance process is consistently aligned across all six regional paradigms, highlighting a critical lack of universal standards.
• The case study on ChatGPT reveals substantial deficiencies in process coverage, indicating misalignment with comprehensive governance expectations.
• The analysis confirms that current governance models lack coherence and consistency, necessitating a harmonized framework for effective global governance.
• The H-GenAIGF framework provides a structured, evidence-based model to unify and strengthen global GenAI governance.