ekip response to the Targeted initiative for a better copyright environment for European creativity and innovation: Call for evidence for an impact assessment
Year: 2026
Type: article
Abstract: This Response provides information on how the gallery, library, archives and museum (GLAM) sector is engaging with AI, with a focus narrowed to the impacts on this sector by commercial AI scraping and specific challenges and recommendations around this. The infrastructures that sustain open digital heritage are currently facing an acute and largely unacknowledged threat: since around 2022, and intensifying dramatically from 2024 onward, cultural heritage institutions have experienced an exponential increase in automated bot traffic from artificial intelligence (AI) companies seeking to scrape data to train generative systems, such as Large Language Models (LLMs). This activity disregards the technical protocols that have governed the open web since its early years and inflicts measurable costs on public and taxpayer-funded institutions. The public heritage sector is seeing a large transfer of value to private corporations largely without consent, attribution, or remuneration. Current research and policy debates on AI and the cultural sector are largely dominated by copyright and licensing questions, which risks overlooking the infrastructural and material dimensions of the crisis and the specific vulnerability of publicly funded, digital heritage infrastructure. This response draws on interviews and workshops with 88 stakeholders from cultural heritage, GLAM, open culture and academic sectors as part of the University of Edinburgh's original and ongoing research, carried out in 2026. This response reports on research findings in two parts: 1) current harms of automated AI extraction on the cultural heritage sector and 2) this sectors ideas for future paths forward.
Institutions: University of Edinburgh, University of Glasgow
Language: English
Subfield: Law
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225327014