Artificial Surrealism and Authors’ Rights
Bibliographic Data
| ID | 13108618 |
|---|---|
| Authors | Artha Dermawan (0000-0003-4357-4656, University of Lapland, corresponding author) |
| Year | 2025 |
| Volume | 11 |
| Issue | 1 |
| Pages | 179-179 |
| Publication date | 2025-09-05 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Santander Art and Culture Law Review (JOURNAL) |
| Journal identifiers | ISSN: 2391-7997 • E-ISSN: 2450-050X |
| Publisher | Wydawnictwo Uniwersytetu Jagiellońskiego (PUBLISHER • PL) |
| DOI | 10.4467/2450050xsnr.25.009.21965 |
| OpenAlex | W4414018057 |
| Language | EN |
| References cited | 36 |
As the world contends with social polarization and the erosion of democratic institutions, the stakes are higher than ever. Yet, despite these pressing challenges the future continues to be predominantly framed in economic terms. For instance, in the 400 pages of the Draghi report in Europe: art, creativity, and creative professionals remain sidelined, viewed as “weak” or “less relevant” portfolios. They are conspicuously absent from major global frameworks, such as the Sustainable Development Goals, the Green Deal, and the Democracy Action Plan. Meanwhile, Artificial Surrealism is already transforming the creative industries, posing new challenges to authors and human creativity. This article addresses the challenges posed by Artificial Intelligence (AI), particularly in the context of text and data mining (TDM). It provides an in-depth analysis of the European Union’s legislative response to TDM exceptions under the Copyright Digital Single Market Directive and highlights the need for a more effective remuneration system for authors. The article applauds the establishment of a Collective Licensing with Extended Effect (CLEE) regime, proposing a Training Data Remuneration Scheme administered by Collective Management Organizations (CMOs). Such a regime aims to secure fair compensation for authors whose works are used in training AI models. For the royalty distribution model, the article proposes a pro-rata calculation based on each author’s contribution to the AI training datasets, ensuring an equitable and transparent allocation of revenue
Law and economics · Political science · Sociology · Computer Science · Law, AI, and Intellectual Property
| Citation velocity | historical |
|---|---|
| Highly cited | No |