Can Licensing Mitigate the Negative Implications of Commercial Web Scraping?

Li, Hanlin and Vincent, Nicholas and Jernite, Yacine and Merrill, Nick and Benjamin, Jesse Josua and Tarkowski, Alek (2023) Can Licensing Mitigate the Negative Implications of Commercial Web Scraping? In: CSCW 2023 Companion - Conference Companion Publication of the 2023 Computer Supported Cooperative Work and Social Computing :. Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW . ACM, New York, pp. 553-555. ISBN 9798400701290

Text (cscw23h-sub1033-i5)
cscw23h-sub1033-i5.pdf - Accepted Version
Download (389kB)

Abstract

The rise of prominent AI models such as ChatGPT and Stable Diffusion has brought the scale of commercial web scraping to the forefront attention of content creators and researchers. Billions of webpages and images are used to train these models without content creators’ knowledge, sparking extensive criticism and even lawsuits against AI firms. Amidst such debates, licensing is proposed by researchers and legal experts to be a potential approach to mitigate content creators’ concerns and promote more responsible data reuse. However, it remains unclear what specific licensing terms will be effective to mitigate content creators’ concerns and what sociotechnical environments are necessary to facilitate the use of licensing at scale. This workshop will provide a venue for researchers, content creators, and legal experts to answer these questions.