Keyword: machine learning

Generative AI and Creative Commons Licences – The Application of Share Alike Obligations to Trained Models, Curated Datasets and AI Output external link

Szkalej, K. & Senftleben, M.
JIPITEC, vol. 15, iss. : 3, 2024

Abstract

This article maps the impact of Share Alike (SA) obligations and copyleft licensing on machine learning, AI training, and AI-generated content. It focuses on the SA component found in some of the Creative Commons (CC) licences, distilling its essential features and layering them onto machine learning and content generation workflows. Based on our analysis, there are three fundamental challenges related to the life cycle of these licences: tracing and establishing copyright-relevant uses during the development phase (training), the interplay of licensing conditions with copyright exceptions and the identification of copyright-protected traces in AI output. Significant problems can arise from several concepts in CC licensing agreements (‘adapted material’ and ‘technical modification’) that could serve as a basis for applying SA conditions to trained models, curated datasets and AI output that can be traced back to CC material used for training purposes. Seeking to transpose Share Alike and copyleft approaches to the world of generative AI, the CC community can only choose between two policy approaches. On the one hand, it can uphold the supremacy of copyright exceptions. In countries and regions that exempt machine-learning processes from the control of copyright holders, this approach leads to far-reaching freedom to use CC resources for AI training purposes. At the same time, it marginalises SA obligations. On the other hand, the CC community can use copyright strategically to extend SA obligations to AI training results and AI output. To achieve this goal, it is necessary to use rights reservation mechanisms, such as the opt-out system available in EU copyright law, and subject the use of CC material in AI training to SA conditions. Following this approach, a tailor-made licence solution can grant AI developers broad freedom to use CC works for training purposes. In exchange for the training permission, however, AI developers would have to accept the obligation to pass on – via a whole chain of contractual obligations – SA conditions to recipients of trained models and end users generating AI output.

ai, Copyright, creative commons, Licensing, machine learning

Bibtex

Article{nokey, title = {Generative AI and Creative Commons Licences – The Application of Share Alike Obligations to Trained Models, Curated Datasets and AI Output}, author = {Szkalej, K. and Senftleben, M.}, url = {https://www.jipitec.eu/jipitec/article/view/415}, year = {2024}, date = {2024-12-13}, journal = {JIPITEC}, volume = {15}, issue = {3}, pages = {}, abstract = {This article maps the impact of Share Alike (SA) obligations and copyleft licensing on machine learning, AI training, and AI-generated content. It focuses on the SA component found in some of the Creative Commons (CC) licences, distilling its essential features and layering them onto machine learning and content generation workflows. Based on our analysis, there are three fundamental challenges related to the life cycle of these licences: tracing and establishing copyright-relevant uses during the development phase (training), the interplay of licensing conditions with copyright exceptions and the identification of copyright-protected traces in AI output. Significant problems can arise from several concepts in CC licensing agreements (‘adapted material’ and ‘technical modification’) that could serve as a basis for applying SA conditions to trained models, curated datasets and AI output that can be traced back to CC material used for training purposes. Seeking to transpose Share Alike and copyleft approaches to the world of generative AI, the CC community can only choose between two policy approaches. On the one hand, it can uphold the supremacy of copyright exceptions. In countries and regions that exempt machine-learning processes from the control of copyright holders, this approach leads to far-reaching freedom to use CC resources for AI training purposes. At the same time, it marginalises SA obligations. On the other hand, the CC community can use copyright strategically to extend SA obligations to AI training results and AI output. To achieve this goal, it is necessary to use rights reservation mechanisms, such as the opt-out system available in EU copyright law, and subject the use of CC material in AI training to SA conditions. Following this approach, a tailor-made licence solution can grant AI developers broad freedom to use CC works for training purposes. In exchange for the training permission, however, AI developers would have to accept the obligation to pass on – via a whole chain of contractual obligations – SA conditions to recipients of trained models and end users generating AI output.}, keywords = {ai, Copyright, creative commons, Licensing, machine learning}, }

Toward a Critique of Algorithmic Violence external link

Bellanova, R., Irion, K., Lindskov Jacobsen, K., Ragazzi, F., Saugmann, R. & Suchman, L.
International Political Sociology, vol. 15, num: 1, pp: 121–150, 2021

Abstract

Questions about how algorithms contribute to (in)security are under discussion across international political sociology. Building upon and adding to these debates, our collective discussion foregrounds questions about algorithmic violence. We argue that it is important to examine how algorithmic systems feed (into) specific forms of violence, and how they justify violent actions or redefine what forms of violence are deemed legitimate. Bringing together different disciplinary and conceptual vantage points, this collective discussion opens a conversation about algorithmic violence focusing both on its specific instances and on the challenges that arise in conceptualizing and studying it. Overall, the discussion converges on three areas of concern—the violence undergirding the creation and feeding of data infrastructures; the translation processes at play in the use of computer/machine vision across diverse security practices; and the institutional governing of algorithmic violence, especially its organization, limitation, and legitimation.

affordences, algorithmic violence, Artificial intelligence, cloud computing, frontpage, governance, harm, interdisciplinary, machine learning

Bibtex

Article{Bellanova2021, title = {Toward a Critique of Algorithmic Violence}, author = {Bellanova, R. and Irion, K. and Lindskov Jacobsen, K. and Ragazzi, F. and Saugmann, R. and Suchman, L.}, doi = {https://doi.org/https://doi.org/10.1093/ips/olab003}, year = {0329}, date = {2021-03-29}, journal = {International Political Sociology}, volume = {15}, number = {1}, pages = {121–150}, abstract = {Questions about how algorithms contribute to (in)security are under discussion across international political sociology. Building upon and adding to these debates, our collective discussion foregrounds questions about algorithmic violence. We argue that it is important to examine how algorithmic systems feed (into) specific forms of violence, and how they justify violent actions or redefine what forms of violence are deemed legitimate. Bringing together different disciplinary and conceptual vantage points, this collective discussion opens a conversation about algorithmic violence focusing both on its specific instances and on the challenges that arise in conceptualizing and studying it. Overall, the discussion converges on three areas of concern—the violence undergirding the creation and feeding of data infrastructures; the translation processes at play in the use of computer/machine vision across diverse security practices; and the institutional governing of algorithmic violence, especially its organization, limitation, and legitimation.}, keywords = {affordences, algorithmic violence, Artificial intelligence, cloud computing, frontpage, governance, harm, interdisciplinary, machine learning}, }

Implementing User Rights for Research in the Field of Artificial Intelligence: A Call for International Action external link

Flynn, S., Geiger, C., Quintais, J., Margoni, T., Sag, M., Guibault, L. & Carroll, M.
European Intellectual Property Review, vol. 2020, num: 7, 2020

Abstract

Last year, before the onset of a global pandemic highlighted the critical and urgent need for technology-enabled scientific research, the World Intellectual Property Organization (WIPO) launched an inquiry into issues at the intersection of intellectual property (IP) and artificial intelligence (AI). We contributed comments to that inquiry, with a focus on the application of copyright to the use of text and data mining (TDM) technology. This article describes some of the most salient points of our submission and concludes by stressing the need for international leadership on this important topic. WIPO could help fill the current gap on international leadership, including by providing guidance on the diverse mechanisms that countries may use to authorize TDM research and serving as a forum for the adoption of rules permitting cross-border TDM projects.

Artificial intelligence, Auteursrecht, frontpage, machine learning, tdm, text and data mining

Bibtex

Article{Flynn2020b, title = {Implementing User Rights for Research in the Field of Artificial Intelligence: A Call for International Action}, author = {Flynn, S. and Geiger, C. and Quintais, J. and Margoni, T. and Sag, M. and Guibault, L. and Carroll, M.}, url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3578819}, year = {0421}, date = {2020-04-21}, journal = {European Intellectual Property Review}, volume = {2020}, number = {7}, pages = {}, abstract = {Last year, before the onset of a global pandemic highlighted the critical and urgent need for technology-enabled scientific research, the World Intellectual Property Organization (WIPO) launched an inquiry into issues at the intersection of intellectual property (IP) and artificial intelligence (AI). We contributed comments to that inquiry, with a focus on the application of copyright to the use of text and data mining (TDM) technology. This article describes some of the most salient points of our submission and concludes by stressing the need for international leadership on this important topic. WIPO could help fill the current gap on international leadership, including by providing guidance on the diverse mechanisms that countries may use to authorize TDM research and serving as a forum for the adoption of rules permitting cross-border TDM projects.}, keywords = {Artificial intelligence, Auteursrecht, frontpage, machine learning, tdm, text and data mining}, }