Eve, Martin Paul (2027) Rage Against the Machine: The Politics of Open Access, Large Language Models, and the Reaction Against Open. In: Groes, Bas and Goody, Alex and Nakamura, Asami (eds.) Digital Inequality: Transcultural Perspectives on the Digital Divide from the Arts, Humanities, and Sciences. Cambridge: Open Book Publishers. (Submitted)
Abstract
For over two decades now, a select group of scientists and researchers have called for academic publishing to harness the digital abundance of the internet and the world wide web. This movement – dubbed "open access" – has called for the open and free availability and re-use of scholarly material. For the longest time, the key arguments for such access centred not only on the epistemic congruence of open practices with research itself, but also on the gross iniquities in scholarly communications on the global stage, fuelled by a voracious for-profit publishing industry that valued returns over scholarship. Meanwhile, governments expressed the more neoliberal view that business access to OA would fuel national economic growth and make academia serve industry, leading to a conflicted political history. In recent days, however, open-access scholarly material has served as the engine that powers the training of so-called AI large language models (LLMs). This has prompted outrage and kickback among substantial portions of the academic and creative community who seek a re-entrenchment of strong copyright principles to guard against AI systems; a rage against the machine. This chapter explores the contradictory motivations and methods of the OA movement and argues that LLMs were an easily foreseen consequence and extension of text and data mining practices. It closes with suggestions of ways to reconcile openness for humanity with the brave new world of artificial intelligence.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Humanities and Social Sciences > School of Creative Arts, Culture and Communication |
| Depositing User: | Martin Eve |
| Date Deposited: | 11 Apr 2025 08:06 |
| Last Modified: | 11 Apr 2025 08:06 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/55407 |
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