Open Access & Copyright in the age of AI: Open Access Week 2025-10-16
Bibliographic record
Abstract
In an age when artificial intelligence is reshaping the way we access, create, and share knowledge, it becomes crucial to reflect on the evolving relationship between Open Access and copyright. As AI systems become increasingly sophisticated in searching, analysing, and generating academic content, they offer new opportunities for research dissemination—but also raise complex legal and ethical challenges concerning authors’ rights and the equitable access to information. These topics were explored during the workshop Open Access & Copyright in the Age of AI, organized by Bocconi Library & Archives and Hertie School Library within the framework of the International Open Access Week 2025, in collaboration with the CIVICA university network. The event featured three keynote presentations: Christopher Landes (Hertie School) discussed how AI is transforming the way we search for, analyse, and publish academic literature; Christine Daoutis (University College London) addressed the implications of copyright in the age of AI for academic researchers; and Nicola Lucchi (Pompeu Fabra University) examined the copyright challenges related to AI training and the notion of lawful use. The workshop concluded with a round table discussion involving Nicolò Cavalli (Bocconi University), Joanna Bryson (Hertie School), and Paola Corti (SPARC Europe), who reflected on the balance between innovation, legal frameworks, and the principles of open science in an AI-driven research environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.023 | 0.010 |
| Insufficient payload (model declined to judge) | 0.117 | 0.039 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".