Break It 'Til You Make It: An Exploration of the Ramifications of Copyright Liability Under a Pre-training Paradigm of AI Development
Name
3614407.3643707.pdf
Size
1.09 MB
Format
Adobe PDF
Checksum (MD5)
c3754fdaa9d26f4f135d2919590db9e6
Author(s)
Yew, Rui-Jie
Date Issued
March 12, 2024
Publisher
ACM
Citation
Yew, Rui-Jie. 2024. "Break It 'Til You Make It: An Exploration of the Ramifications of Copyright Liability Under a Pre-training Paradigm of AI Development."
Version
Final published version
Abstract
This paper considers the potential impacts of a pre-training regime on the application of copyright law for AI systems. Proposed evaluations of the use of copyrighted works for AI training have assumed a tight integration between model training and model deployment: the model's application plays a central role in determining if a training procedure's use of copyrighted data infringes on the author's rights. In practice, however, large, modern AI systems are increasingly built and deployed under a pre-training paradigm: large models may be trained for a multitude of applications and then subsequently specialized toward specific ones. Thus, I consider copyright's indirect liability doctrine to consider the effect of copyright on the current market structures involved in the development and deployment of AI systems. The main contribution of this paper lies in its analysis of what indirect copyright liability litigation for technologies in the past have to say for how AI companies may manage or attempt to limit their copyright liability in practice. Based on this analysis, I conclude with a discussion of strategies to close these loopholes and of the role that copyright law has to play within the AI policy landscape.
Description
CSLAW ’24, March 12–13, 2024, Boston, MA, USA
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NoDerivs
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3614407.3643707
