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Can Copyright Be Reduced to Privacy?

Authors Niva Elkin-Koren, Uri Hacohen, Roi Livni, Shay Moran

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Author Details

Niva Elkin-Koren
  • Faculty of Law, Tel Aviv University, Israel
Uri Hacohen
  • Faculty of Law, Tel Aviv University, Israel
Roi Livni
  • School of Electrical Engineering, Tel Aviv University, Israel
Shay Moran
  • Departments of Mathematics and Computer Science, Technion, Haifa, Israel

Funding

This research was funded in part by and ISF Grant (2188$$120), an ERC grant (FOG, 101116258) and ERC grant (GENERALIZATION, 10139692). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. In addition, the research leading to these results was supported by TILabs Tel-Aviv University Innovation Labs.

Acknowledgements

We thank Bruria Friedman for research assistance.

Cite AsGet BibTex

Niva Elkin-Koren, Uri Hacohen, Roi Livni, and Shay Moran. Can Copyright Be Reduced to Privacy?. In 5th Symposium on Foundations of Responsible Computing (FORC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 295, pp. 3:1-3:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.FORC.2024.3

Abstract

There is a growing concern that generative AI models will generate outputs closely resembling the copyrighted materials for which they are trained. This worry has intensified as the quality and complexity of generative models have immensely improved, and the availability of extensive datasets containing copyrighted material has expanded. Researchers are actively exploring strategies to mitigate the risk of generating infringing samples, with a recent line of work suggesting to employ techniques such as differential privacy and other forms of algorithmic stability to provide guarantees on the lack of infringing copying. In this work, we examine whether such algorithmic stability techniques are suitable to ensure the responsible use of generative models without inadvertently violating copyright laws. We argue that while these techniques aim to verify the presence of identifiable information in datasets, thus being privacy-oriented, copyright law aims to promote the use of original works for the benefit of society as a whole, provided that no unlicensed use of protected expression occurred. These fundamental differences between privacy and copyright must not be overlooked. In particular, we demonstrate that while algorithmic stability may be perceived as a practical tool to detect copying, such copying does not necessarily constitute copyright infringement. Therefore, if adopted as a standard for detecting an establishing copyright infringement, algorithmic stability may undermine the intended objectives of copyright law.

Subject Classification

ACM Subject Classification
  • Social and professional topics → Copyrights
Keywords
  • Copyright
  • Privacy
  • Generative Learning

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