{"id":776,"date":"2025-04-15T20:59:37","date_gmt":"2025-04-15T20:59:37","guid":{"rendered":"https:\/\/www.techrepublic.com\/?p=4302032"},"modified":"2025-04-15T20:59:37","modified_gmt":"2025-04-15T20:59:37","slug":"uk-ai-copyright-rules-may-backfire-causing-biased-models-low-creator-returns","status":"publish","type":"post","link":"https:\/\/blog.tecnoartesanos.com\/index.php\/2025\/04\/15\/uk-ai-copyright-rules-may-backfire-causing-biased-models-low-creator-returns\/","title":{"rendered":"UK AI Copyright Rules May Backfire, Causing Biased Models & Low Creator Returns"},"content":{"rendered":"
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Image: pichetw\/Envato Elements<\/figcaption><\/figure>\n

Barring companies like OpenAI, Google, and Meta from training AI on copyrighted material in the UK may undermine model quality and economic impact, policy experts warn. They say that it will lead to bias in model outputs, undermining their effectiveness, while rightsholders are unlikely to receive the level of compensation they anticipate.<\/p>\n

The UK government opened a consultation<\/a> in December 2024 to explore ways to protect the rights of artists, writers, and composers when creative content is used to train AI models. It outlined a system that permits AI developers to use online content for training unless the rightsholder explicitly opts out.<\/p>\n

Bodies representing the creative industries largely rejected this proposal, as it put the onus on creators to exclude their content rather than requiring AI developers to seek consent. Tech companies<\/a> didn\u2019t like it either, arguing that the system would make it difficult to determine which content they could legally use, restrict commercial applications, and demand excessive transparency.<\/p>\n

During a recent webinar hosted by the Centre for Data Innovation think tank, three policy experts explain why they believe any solution short of a full text and data mining exemption in UK copyright law risks producing ineffective AI systems and stalling innovation.<\/p>\n

<\/span>Opt-out regimes may result in poorly trained AI and minimal income for rightsholders<\/span><\/h2>\n

Benjamin White, the founder of copyright reform advocacy group Knowledge Rights 21, argued that regulations on AI training will affect more than just the creative industries, and since copyright serves to stimulate investment by protecting intellectual property, he said the broader economic impact of any restrictions should also be taken into account. \u201cThe rules that affect singers affect scientists, and the rules that affect clinicians, affect composers as well. Copyrights are sort of a horizontal one-size-fits-all all,\u201d he said.<\/p>\n

He added that the scientific community is \u201cvery concerned at the framing of the consultation,\u201d noting that it overlooks the potential benefits of knowledge sharing in advancing academic research, which, in turn, offers widespread advantages for society and the economy.<\/p>\n

White said: \u201cThe existing exception doesn\u2019t allow universities to share training data or analysis data with other universities within proportionate partnerships, doesn\u2019t allow NHS trusts to share training data derived from copyright materials like journal articles or materials scraped off the web.\u201d<\/p>\n

SEE: Why Artists Hate AI Art<\/a><\/strong><\/p>\n

Bertin Martens, senior fellow at economic think tank Bruegel, added: \u201cI think media industries want to have their cake and eat it at the same time. They\u2019re all using these models to increase their own productivity already at this moment, and they benefit from good quality models, and by withholding their data for training, they reduce the quality\u2026 so it cuts into their own flesh.\u201d<\/p>\n

If AI developers signed licensing agreements with just the consenting publishers or rightsholders, then the data their models are trained on would be skewed, according to Martens. \u201cClearly, even big AI companies are not going to sign licenses along that long tail of small publishers,\u201d he said. \u201cIt\u2019s far too costly in terms of transaction costs, it\u2019s not feasible, and so we get biased models with partial information.\u201d<\/p>\n

Julia Willemyns, the co-founder of tech policy research project UK Day One, stated that the opt-out regime is unlikely to be effective in practice, as jurisdictions with less restrictive laws will still allow access to the same content for training. Blocking access to outputs from those jurisdictions would ultimately deprive the UK of the best available models, she warned. She said this \u201cslows down technology diffusion\u201d and has \u201cnegative productivity effects.\u201d<\/p>\n

SEE: UK Government Releases AI Action Plan<\/a><\/strong><\/p>\n

Furthermore, artists are unlikely to earn meaningful income from AI licensing deals. \u201cThe problem is that every piece of data isn\u2019t worth very much to the models, these models operate at scale,\u201d said Willemyns. Even if licensing regimes were enforced globally and rightsholders\u2019 content could only be used with explicit legal consent, the economic benefit for creators would still be \u201clikely very, very minimal.\u201d \u201cSo, we\u2019re trading off countrywide economic effects for a positive that seems very negligible,\u201d she said.<\/p>\n

Willemyns added that overcomplicating the UK\u2019s copyright approach by, say, requiring separate regimes for AI training on scientific and creative materials, could create legal uncertainty. This would overburden courts, deter business adoption, and risk losing out on AI\u2019s productivity gains. A text and data mining exemption would ensure simplicity.<\/p>\n