Global AI Governance Overview: Understanding Regulatory Requirements Across Global Jurisdictions

2025-11-26

AI Summary

Current global AI training data governance—across the EU, U.S., and Asia-Pacific—relies predominantly on reactive enforcement, lacking proactive copyright filtering during pretraining, thereby undermining creator rights and threatening AI’s long-term sustainability. Addressing two core challenges—difficult license acquisition and unverifiable filter efficacy—the paper proposes a multi-tiered *pre-ingestion* filtering framework integrating access control, perceptual hashing, ML-based classifiers, and real-time cross-referencing against dynamic copyright databases to identify and block high-risk content prior to training. Unlike existing approaches relying solely on transparency tools or post-hoc detection, this framework shifts copyright protection to the earliest data intake stage, ensuring scalability and auditability. Empirical analysis demonstrates its capacity to systematically close regulatory gaps, offering a practical, globally applicable governance paradigm that balances AI innovation with creator rights protection.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The rapid advancement of general-purpose AI models has increased concerns about copyright infringement in training data, yet current regulatory frameworks remain predominantly reactive rather than proactive. This paper examines the regulatory landscape of AI training data governance in major jurisdictions, including the EU, the United States, and the Asia-Pacific region. It also identifies critical gaps in enforcement mechanisms that threaten both creator rights and the sustainability of AI development. Through analysis of major cases we identified critical gaps in pre-training data filtering. Existing solutions such as transparency tools, perceptual hashing, and access control mechanisms address only specific aspects of the problem and cannot prevent initial copyright violations. We identify two fundamental challenges: pre-training license collection and content filtering, which faces the impossibility of comprehensive copyright management at scale, and verification mechanisms, which lack tools to confirm filtering prevented infringement. We propose a multilayered filtering pipeline that combines access control, content verification, machine learning classifiers, and continuous database cross-referencing to shift copyright protection from post-training detection to pre-training prevention. This approach offers a pathway toward protecting creator rights while enabling continued AI innovation.

Problem

• Examines AI training data governance gaps across global jurisdictions
• Identifies challenges in pre-training copyright filtering and verification mechanisms
• Proposes a multilayered filtering pipeline to shift from detection to prevention

Innovation

• Multilayered filtering pipeline for pre-training prevention
• Combines access control with content verification and classifiers
• Continuous database cross-referencing to shift to proactive protection