The Institutional Alignment Challenge: Grappling with AI in Collections
Bibliographic record
Abstract
With the rise of publicly available generative artificial intelligence (GenAI) tools, librarians have been working to understand their impact on library-licensed e-resources while ensuring users receive accurate, timely guidance on privacy, intellectual property and other usage considerations. In response to this challenge, the University of Toronto Libraries (UTL) formed its Collections AI Response Team, to bring together expertise across collections and scholarly communications units and to strategize on how to best mitigate the risks posed by these tools to our institution, its members and the environment, while avoiding the potential stifling of innovative uses of these technologies by our community. So far, the Team’s efforts have focused on three areas: Collaboration and alignment with institution-wide working groups developing UofT’s response to GenAI ; Outreach to instructors and researchers via workshops and consultations, with a focus on licensing and IP ownership; the creation of our GenAI Tool Navigator, to gather and proliferate information about GenAI tools as they become available to users. As our institution’s approach to the ethical and practical implications of GenAI tools continue to evolve and reflect the diversity of approaches to GenAI within it, our Team has had to anticipate what the role of Collections should be in this conversation. The presentation will share our insights from this work so far, including the increased saliency of issues of privacy and openness in this work, as well as share our plans for future initiatives in this area.
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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 | 0.100 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.036 | 0.056 |
| Scholarly communication | 0.070 | 0.053 |
| Open science | 0.008 | 0.057 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".