dorsal/arxiv
View SchemaResearch Integrity and Academic Authority in the Age of Artificial Intelligence: From Discovery to Curation?
| Authors | Simon Chesterman, Loy Hui Chieh |
|---|---|
| Categories | |
| ArXiv ID | 2601.05574vv1 |
| URL | https://arxiv.org/abs/2601.05574 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Artificial intelligence is reshaping the organization and practice of research in ways that extend far beyond gains in productivity. AI systems now accelerate discovery, reorganize scholarly labour, and mediate access to expanding scientific literatures. At the same time, generative models capable of producing text, images, and data at scale introduce new epistemic and institutional vulnerabilities. They exacerbate challenges of reproducibility, blur lines of authorship and accountability, and place unprecedented pressure on peer review and editorial systems. These risks coincide with a deeper political-economic shift: the centre of gravity in AI research has moved decisively from universities to private laboratories with privileged access to data, compute, and engineering talent. As frontier models become increasingly proprietary and opaque, universities face growing difficulty interrogating, reproducing, or contesting the systems on which scientific inquiry increasingly depends. This article argues that these developments challenge research integrity and erode traditional bases of academic authority, understood as the institutional capacity to render knowledge credible, contestable, and independent of concentrated power. Rather than competing with corporate laboratories at the technological frontier, universities can sustain their legitimacy by strengthening roles that cannot be readily automated or commercialized: exercising judgement over research quality in an environment saturated with synthetic outputs; curating the provenance, transparency, and reproducibility of knowledge; and acting as ethical and epistemic counterweights to private interests. In an era of informational abundance, the future authority of universities lies less in maximizing discovery alone than in sustaining the institutional conditions under which knowledge can be trusted and publicly valued.
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"abstract": "Artificial intelligence is reshaping the organization and practice of research in ways that extend far beyond gains in productivity. AI systems now accelerate discovery, reorganize scholarly labour, and mediate access to expanding scientific literatures. At the same time, generative models capable of producing text, images, and data at scale introduce new epistemic and institutional vulnerabilities. They exacerbate challenges of reproducibility, blur lines of authorship and accountability, and place unprecedented pressure on peer review and editorial systems. These risks coincide with a deeper political-economic shift: the centre of gravity in AI research has moved decisively from universities to private laboratories with privileged access to data, compute, and engineering talent. As frontier models become increasingly proprietary and opaque, universities face growing difficulty interrogating, reproducing, or contesting the systems on which scientific inquiry increasingly depends.\n This article argues that these developments challenge research integrity and erode traditional bases of academic authority, understood as the institutional capacity to render knowledge credible, contestable, and independent of concentrated power. Rather than competing with corporate laboratories at the technological frontier, universities can sustain their legitimacy by strengthening roles that cannot be readily automated or commercialized: exercising judgement over research quality in an environment saturated with synthetic outputs; curating the provenance, transparency, and reproducibility of knowledge; and acting as ethical and epistemic counterweights to private interests. In an era of informational abundance, the future authority of universities lies less in maximizing discovery alone than in sustaining the institutional conditions under which knowledge can be trusted and publicly valued.",
"arxiv_id": "2601.05574",
"authors": [
"Simon Chesterman",
"Loy Hui Chieh"
],
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"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Research Integrity and Academic Authority in the Age of Artificial Intelligence: From Discovery to Curation?",
"url": "https://arxiv.org/abs/2601.05574",
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