Control and Compensation. A Comparative Analysis of Copyright ...
Lawmakers and administrative agencies around the globe are debating whether the use of copyrighted content for AI training does or should require the rights holder’s consent. This article examines legislation and policy debates in various countries including the U.S., Canada, the UK, the EU, Israel, China, Singapore, and Japan. The discussion is centered around issues of control, compensation, transparency, and legal certainty.
Theses on Copyright and AI Training
In this article, the author presents six theses regarding the current landscape of copyright and AI training:
- The discussion is dominated by four factors: control, compensation, transparency, and legal certainty.
- Control is a prerequisite for compensation in the legal systems examined.
- Countries are trying to recalibrate the balance of interests favoring either AI companies or rights holders.
- Copyright-related transparency obligations empower rights holders.
- The EU provides favorable conditions for AI companies in terms of legal certainty.
- Copyright law is not the decisive factor in determining the location of AI training facilities.
Key Aspects of Copyright and AI Training
Legal and policy discussions on using copyrighted content for AI training primarily focus on four aspects: control, compensation, transparency, and legal certainty. Control refers to whether rights holders can dictate the use of their content for AI training. Compensation pertains to whether rights holders can claim remuneration for such use.
Transparency plays a crucial role in enforcing control and compensation as it enables rights holders to identify violations of their copyright. Legal certainty reduces uncertainty for both rights holders and AI companies, impacting strategic business decisions and transaction costs.
Control and Compensation in Different Legal Systems
Legal systems vary in terms of the control rights granted to rights holders. Some countries, like China, provide full control without exceptions for AI training uses. In contrast, the UK has introduced exceptions for text and data mining, with ongoing discussions on commercial TDM and AI training.
While contractual rights to compensation are often tied to control rights, statutory schemes may also provide for indirect forms of compensation. Transparency requirements enhance rights holders' ability to enforce control and claim compensation for AI training use.
In conclusion, the interplay between control, compensation, transparency, and legal certainty in copyright and AI training underscores the complex balancing act between AI innovation and rights protection.
