Abstract: This article investigates how the legal basis for artificial intelligence (AI) training is framed within data protection guidelines and regulatory interventions, drawing on a comparative analysis of approaches taken by authorities across multiple jurisdictions. Focusing on the European Union’s General Data Protection Regulation and analogous data protection frameworks globally, the study systematically maps guidance, statements, and actions to identify areas of convergence and divergence in the conceptualization and operationalization of lawful grounds for personal data processing—particularly legitimate interest and consent—in the context of AI model development. The analysis reveals a trend towards converging on the recognition of legitimate interest as the predominant legal basis for AI training. However, this convergence is largely superficial, as guidelines rarely resolve deeper procedural and substantive ambiguities, and enforcement interventions often default to minimal safeguards. This disconnect between regulatory rhetoric and practical compliance leaves significant gaps in protection and operational clarity for data controllers, calling into question the reliability and legitimacy of the existing framework for lawful AI training. It warns that, without clearer operational standards and more coherent cross-border enforcement, there is a risk that legal bases such as legitimate interest will serve as little more than formalities. Reflecting on these findings, the article explores the prospects and limitations for achieving greater alignment or harmonization at the global level. Specifically, it reflects on pathways for global AI governance, emphasizing that the progress would benefit from distinguishing issues amenable to international convergence from those that require context-sensitive, locally adaptive solutions. It considers how regulators, practitioners, civil society activists, and scholars might leverage these insights to prioritize evidence-based avenues rather than seeking uniformity at the conceptual level for its own sake, with the ultimate goal of advancing both principled and practical frameworks for lawful AI training across diverse legal landscapes.
Authors: Wenlong Li, Yueming Zhang, Qingqing Zheng, Aolan Li
Source: Li, W., Zhang, Y., Zheng, Q., & Li, A. (2026). How the Legal Basis for AI Training is Framed in Data Protection Guidelines and Interventions: Comparative Perspectives and the Prospect of Global Convergence. International Data Privacy Law, 16(2)
Publication date: June 2026
Original Link: 
数据保护指南与监管干预如何界定人工智能训练的法律依据——比较视角与全球趋同前景.pdf
