Copyright in AI Pre-Training Data Filtering: Regulatory Landscape and Mitigation Strategies

2025-11-26

AI Summary

Widespread reliance on unlicensed web-scale data for AI pretraining poses escalating global copyright infringement risks, yet current regulatory approaches remain predominantly reactive, lacking proactive, pre-training compliance mechanisms. Method: We systematically analyze copyright governance frameworks across the EU, U.S., and major Asia-Pacific jurisdictions, identifying structural deficiencies in licensing acquisition, content filtering, and enforcement oversight. To address delayed risk detection and incomplete technical coverage during pretraining, we propose a “Proactive Multi-Layer Filtering Framework” integrating access control, perceptual hashing, ML-based classifiers, dynamic database matching, and transparency tools. Contribution/Results: The framework enables end-to-end identification, blocking, and verifiable mitigation of copyright risks in training data pipelines. It is the first to embed copyright compliance intrinsically into the AI training frontend—establishing a technically feasible, governance-aligned pathway that balances creator rights with sustainable AI development.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Consent frameworks and practices on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 regulatory gaps in AI training data governance
• Identifies challenges in pre-training license collection and content filtering
• Proposes multilayered filtering to shift from detection to prevention

Innovation

• Multilayered filtering pipeline for copyright protection
• Combines access control and content verification mechanisms
• Shifts from post-training detection to pre-training prevention