Standards, frameworks, and legislation for artificial intelligence (AI) transparency

Abstract

The global landscape of transparency standards, frameworks, and legislation for artificial intelligence (AI) shows an increasing focus on building trust, accountability, and ethical deployment. This paper presents comparative analysis of key frameworks for AI transparency, such as the IEEE P7001 standard and the CLeAR Documentation Framework, highlighting how regions like the United States, European Union, China, and Japan are addressing the need for transparent and trustworthy AI systems. Common themes across these standards include the need for tiered transparency levels based on system risk and impact, continuous documentation updates throughout the development and revision processes, and the production of explanations tailored to various stakeholder groups. Several key challenges arise in the development of AI transparency standards, frameworks, and legislation, including balancing transparency with privacy, ensuring intellectual property rights, and addressing security concerns. Promoting adaptable, sector-specific transparency regulatory structures is critical in the development of frameworks flexible enough to keep pace with AI’s rapid technological advancement. These insights contribute to a growing body of literature on how best to develop transparency regulatory structures that not only build trust in AI but also support innovation across industries.