Alek Tarkowski
Research Subjects
Research Interests
Alek Tarkowski has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Hanlin Li
School of Information, University of Texas at Austin, Austin, Texas, USA
1 shared publication - Jesse Josua Benjamin
Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands
1 shared publication - Nicholas Vincent
Computing Science, Simon Fraser University, Burnaby, BC, Canada
1 shared publication - Nick Merrill
Daylight Lab, Center for Long-Term Cybersecurity, University of California, Berkeley, United States
1 shared publication - Yacine Jernite
Hugging Face, United States
1 shared publication
Latest Publications
- Open Access
- 14 October 2023
- Published by Association for Computing Machinery (ACM)
- p. 553-555
Can Licensing Mitigate the Negative Implications of Commercial Web Scraping?
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.
- 14 October 2023
- Published by Association for Computing Machinery (ACM)
- p. 553-555
Open AccessCan Licensing Mitigate the Negative Implications of Commercial Web Scraping?
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.