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Edinburgh College of Art, University of Edinburgh Response to the UK Government Intellectual Property Office Consultation: Copyright and Artificial Intelligence

Authors: Osborne, Nicola; Parkinson, Caroline Anne; Terras, Melissa; Cruz, Juan;

Edinburgh College of Art, University of Edinburgh Response to the UK Government Intellectual Property Office Consultation: Copyright and Artificial Intelligence

Abstract

This document reflects the response provided by Edinburgh College of Art to the UK Government Information Commissioner’s Officer Consultation on Copyright and Artificial Intelligence (AI)[1], which ran from 17th December 2024 until 25th February 2025. Our response was submitted through the formal consultation portal[2], which included all questions. We did not choose to respond to all questions in the consultation, but we include the full list of questions in the Appendix for reference. Our response draws on expertise at Edinburgh College of Art in undertaking leading research in art, design and creative industries, and in training the next generation artists and creators through undergraduate and postgraduate programmes. We also draw on substantial expertise in working in partnership with the creative industries on ambitious large scale creative data and technology innovation programmes, particularly: Creative Informatics (2018-2024) and specifically the Creative AI Demonstrator (2023-4), funded by AHRC (the Arts and Humanities Research Council), the Data Driven Innovation Programme of the Edinburgh City Region Deal, Scottish Funding Council (SFC), and DCMS (the Department of Culture, Media & Sport). CoSTAR Realtime Lab (2023-), funded through UK Research and Innovation (UKRI) Infrastructure Fund and delivered by the AHRC. ekip: European Cultural and Creative Sectors and Industries Policy Platform (2023-), funded by the European Commission, with ECA participation supported under the UKRI Horizon Europe Guarantee Scheme. SummaryThe consultation sought views on how the government can ensure the UK’s legal framework for AI and copyright supports the UK creative industries and AI sector together. In particular, the consultation sought views on four identified options (referred to throughout our response) with the Government’s preference noted as ‘Option 3’: Option 0: Do nothing: Copyright and related laws remain as they are. Option 1: Strengthen copyright requiring licensing in all cases Option 2: A broad data mining exception Option 3: A data mining exception which allows right holders to reserve their rights, underpinned by supporting measures on transparency Our response highlights how proposed copyright changes (Option 3) would negatively impact the creative sector and suggests preferable arrangements (Option 0 with additional considerations) which will better protect the intellectual property rights, business development, and careers of creative enterprises and practitioners. [1] https://www.gov.uk/government/consultations/copyright-and-artificial-intelligence [2] https://ipoconsultations.citizenspace.com/ipo/consultation-on-copyright-and-ai/

Please note that this document, excluding the cover image is shared under Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Do let us know if you find it useful in your own work: designinformatics@ed.ac.uk.

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Keywords

creative industries, Copyright, artificial intelligence

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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