Pro-Copyright, Pro-AI: The Power of Collective Licensing
Anita Huss-Ekerhult & Antonios Baris
What the paper says
The aim of this Article is to showcase the emerging AI licensing solutions pioneered by RROs and to explore how these frameworks address the challenges posed by AI’s reliance on copyrighted works. The discussion begins with a primer on the structure and operation of collective management frameworks, highlighting their effectiveness in managing rights for secondary uses. Following this, the Article examines the necessity of licensing in the AI context, emphasizing why exceptions and limitations under current copyright regimes are insufficient to adequately address the complexities of AI training. This section underscores the limitations of existing legal frameworks and the potential harm to rightsholders if AI systems continue to use copyrighted works without appropriate permissions or compensation. Finally, the Article presents an analysis of the emerging licensing solutions tailored to AI, illustrating how these initiatives by RROs are not only meeting the demands of the AI market but are also paving the way for sustainable and equitable practices at the intersection of copyright and technology.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.