Workshop Series on Generative AI and Copyright Law

Call for Contributions

Generative AI is trained on vast amounts of data. The current practice of collecting the data to train such models rarely includes considerations about copyright and intellectual property rights. While the models themselves and the data they are trained on are often multilingual and multinational, legal requirements and rules vary between jurisdictions. Open questions remain not just about the input data used to train Generative AI, but also about the artifacts it produces, and which rules apply to them (these can include in how far the output is a derivative of existing, specific rules that apply to AI systems (e.g. AI Act), general rules, e.g. with regard to trademarks).

Workshop Series:
The online workshop series on Generative AI and Copyright Law aims to facilitate discussions among researchers and practitioners in law and computer science. The series will consist of one-hour online workshop sessions featuring two 20-minute talks (plus 10-minute Q&A).

Topics of interest include, but are not limited to:

• Legal aspects of Generative AI training and application, including copyright, privacy, cyber-security, information security, etc.
• Use of Generative models and IP protection challenges regarding output (e.g., best practices, license to content attribution, etc.)
• Comparative studies and comparisons of relevant laws applicable to Generative AI in different jurisdictions
• Challenges in the conflict of laws relating to Generative AI
• Copyright law exceptions applied to training Generative AI (e.g., fair use, text and data mining)
• Comparative studies and selected topics on IP infringement cases in the context of Generative AI training
• Author’s and Artist’s rights in the context of Generative AI
• Potential solutions and mitigation strategies (incl. regulatory sandboxes, etc.)
• Topics related to software and data licensing for Generative AI
• Technical frameworks, methods, and approaches to filter datasets based on licenses and detect copyrighted material in Generative AI outputs
• Technical standards and norms addressing and describing legal rules relevant to Generative AI
• Shortcomings of the current legal situation and potential updates required
• Balancing stakeholder interests, especially of content creators vs. users, in view of ubiquitous AI
• Proposals for future policy relating to Generative AI and copyright
• Ethical considerations, including bias and fairness, privacy and data protection, and social and cultural implications of Generative AI on copyright
• International and cross-cultural perspectives on Generative AI and copyright, including the impact on cultural diversity, linguistic diversity, and global knowledge production
• Legal and policy frameworks, intellectual property and competition law issues, liability, and responsibility of developers, users, and intermediaries, regulatory approaches, and international and comparative approaches to Generative AI and copyright law.

We invite different kind of contributions:

• Abstracts (up to 1 page + references)
• Short papers (up to 4 pages + references)

Contributions can be archival or non-archival. Archival contributions should describe novel, unpublished work, non-archival contributions can also describe previously published work and should include citations of the work.

Publication:
All contributions will be presented as part of the workshop series. Submissions can be archival or non-archival. Non-archival submissions will only be used to review proposed talks and not published. Archival submissions will be published as part of workshop proceedings under the CC-BY-4.0 license on Zenodo.

Submissions will be reviewed in a single-blind peer-review process.

For archival submissions please use the ACL template (Word / LaTeX).

Dates:
Submission Deadline: 23^rd of August 2024
Workshop Sessions: September to October 2024 (exact dates tba)
(All deadlines are at 23:59:59 AoE (Anywhere on Earth), UTC-12)

Submission:
Please send your abstract or paper to genai.copyright@gmail.com before the deadline and indicate whether you want your submission to be archival or non/archival.

Organizers:
Daniel Braun, University of Twente (d.braun@utwente.nl)
Baltasar Cevc, fingolex (baltasar.cevc@fingolex.com)
Bernhard Waltl, Liquid Legal Institute (b.waltl@liquid-legal-institute.org)