The anatomy of AI policies: a systematic comparative analysis of AI policies across the globe

Abstract

Abstract The rapid expansion of artificial intelligence (AI) across sectors brings significant benefits but also substantial risks, such as bias, discrimination, and lack of transparency. Mitigating these risks requires AI governance frameworks that ensure ethical and responsible use. While existing studies highlight strategies and ethical guidelines, comparative analyses of emerging responsible AI (RAI) frameworks, standards, and regulations remain limited. This study aims to fill this gap by employing a rapid review methodology to examine 17 responsible AI frameworks, standards, and regulations which we named as AI policies throughout this research, from diverse regions, including Singapore, the US, the UK, Canada, Hiroshima, and Australia, and global organizations including the Organization for Economic Co-operation and Development (OECD), and International Organization for Standardization (ISO). This research aimed to address four primary questions on identifying global and local AI policies, identifying and analyzing their key features, assessing implementation challenges, and determining the essential components for designing an integrated AI governance framework. There are eleven key features identified, including RAI Principles, Stakeholders, Stages (AI software development life cycle), Targeted audiences, Scalability, Enforce-ability, Resource Intensive, Region, Technology, AI governance practices (Prerequisites, outcomes, implementation tools or guides), and AI governance area. The comparative analysis highlighted that while the AI policies offer detailed implementation guidelines, they differ in their approaches, mandatory nature, scalability, and resource demands. These differences are critical for organizations seeking to implement these policies effectively. Challenges related to resource intensity, scalability, governance practices, and ambiguous targeted audiences were noted as significant barriers to successful adoption. Based on the analysis, key components for an RAI framework were proposed, and categorized into qualities (scalable, extensible, adaptive, efficient), dimensions (scope, context, implementation practices), and governance practices (prerequisites/outcomes, resources, governance steps). These components aim to guide organizations in developing AI governance frameworks.

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