Copyright-Centric AI

Copyright-Centric AI refers to generative AI systems designed with copyright protection as a core principle, using quantitative metrics to compensate copyright owners. This framework addresses the copyright challenges of large-scale AI training, ensuring compliance with international copyright standards and ethical AI practices. It leverages techniques from cooperative game theory to be fair and interpretable. This is particularly relevant under the EU AI Act's transparency requirements and the US's evolving copyright litigation landscape. For enterprises, it means moving from reactive risk avoidance to proactive value-sharing models, ensuring long-term access to high-quality training data while minimizing legal exposure. This aligns with ISO/IEC 42001 AI Management System standards, which demand responsible AI development and risk-adjusted innovation。

Implementation typically follows three steps: 1. Data-Centric Inventorying—cataloging all training data with rights-related metadata. 2. Contribution-Based Attribution—applying quantitative methods like Shapley values to assign weight to each data-point' contribution to model outputs. 3. Automated Compensation-Mechanism—integrating these weights into a revenue-sharing or licensing-fee model. For example, a global software company using AI-assisted coding tools could be closely monitored under the EU AI Act; by adopting a copyright-centric approach, they can be closely audited for compliance. Key performance indicators (KPIs) include: reduction in copyright-related legal claims (target: >70%), increase in high-quality data-sourcing efficiency (target: +25%), and AI model-specific compliance score (target: >90/100).

Taiwan enterprises face three primary challenges: 1. Technical Complexity—quantifying data contribution requires specialized expertise. Solution: Partner with AI-focused law firms and technical consultants. 2. Regulatory Fragmentation—different jurisdictions (Taiwan, EU, USA) have different copyright standards. Solution: Implement a modular compliance framework that-adjusts based on the jurisdiction of the AI application. 3. Data-Centric Costs—the computational cost of attribution-based models is high. Solution: Use sampling-based approximation methods to reduce overhead. Priority actions include: Month 1-2: Data-rights audit; Month 3-6: Pilot attribution model; Month 7-12: Full-scale deployment. This roadmap ensures the company meets both the Taiwan Copyright Act and international standards like the AI Act within a year.

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