Academic Libraries as Arbiters of Truth in the Age of AI
In the IFLA Trend Report 2024, David Lankes describes a future in which librarians are no longer just curators of knowledge, but arbiters of reality itself. He outlines a scenario shaped by deepfakes, hallucinated citations, and machine-generated misinformation. The challenge isn’t just too much information, but the erosion of shared reality itself.
Research and academic libraries are uniquely positioned to meet this moment, not by policing the boundaries of truth, but by shaping the environments where truth-seeking can flourish. This work goes beyond AI. It’s about trust, context, and how we manage knowledge across systems, disciplines, and communities.
Here are seven ways libraries might embrace this expanded role:
1. Embrace the Reference Renaissance
When a student or faculty member brings in an AI-generated citation that doesn’t exist, it’s not a failure, it’s an opportunity. The reference or interlibrary loan exchange becomes a site of inquiry and reflection. Instead of simply correcting the error, we might ask: Where did this come from? Why did it seem believable? And how can we nurture habits of discernment in a world where anything can be made to look real? These moments offer foundational knowledge work, and they open space for interdisciplinary conversations about sourcing, authority, and credibility.
2. Verify, Then Visualize
We can go beyond verification and help people see the deeper structures behind knowledge generation. Libraries are increasingly positioned to contribute to tools and frameworks that visualize data provenance, publication networks, peer review histories, and emerging patterns like AI-assisted authorship. While we may not build all the tools ourselves, we bring the curatorial insight, metadata expertise, and cross-disciplinary perspective needed to make these systems meaningful and trustworthy. Imagine a scholar tracing a citation and discovering its entire relational ecosystem, stretching across disciplinary boundaries. This kind of transparent knowledge mapping helps us understand not just what we know, but how that knowledge was created and connected.
3. Supporting Applied AI Literacy for Scholars
As AI tools reshape research practices, students and scholars need more than how-to guides. They need thoughtful, applied frameworks for navigating bias, authorship, data ethics, and evolving methodologies. Libraries can offer a trusted space for this kind of exploration. From advanced prompt design to recommending tools tailored to disciplinary norms, from unpacking algorithmic transparency to exploring the use of synthetic data in research, we can create learning opportunities that help people critically engage with AI in ways that are both intellectually rigorous and practically useful.
4. Surface the Integrity Chain
Behind every trusted article or dataset is a quiet chain of infrastructure, including DOIs, ORCIDs, FAIR data principles, open repositories, and more. Libraries already steward much of this, and now there is an opportunity to bring that integrity infrastructure into clearer view. When someone asks, “How do I know this is real?” we can offer more than just the content. We can provide a window into the systems and standards that uphold its credibility. In doing so, we help build a deeper awareness of the often-invisible scaffolding that supports scholarly and scientific trust.
5. Supporting the Visibility and Context of Diverse Knowledge
As AI systems blend content from across the web, they often obscure the origins and complexity of the ideas they surface. This creates a challenge, and also an opportunity, for academic libraries, archives, and cultural heritage institutions. We can work to ensure that emerging disciplines, gray literature, community-rooted knowledge, and historically marginalized perspectives are not only available, but discoverable in ways that honor their context. This work is grounded in partnership and care. By fostering inclusive and interdisciplinary knowledge systems, libraries help expand what is recognized and ensure that a wider range of voices can meaningfully shape the emerging knowledge landscape.
6. Practicing Openness and Transparency
With systems shaped by black-box algorithms and synthetic content, openness is not just a value, it becomes a vital form of trust-building. Libraries can lead by advocating for openness in the development and deployment of AI, calling for visible processes, clear labeling of AI-generated materials, transparent sourcing, and open documentation of how tools are trained and used. This work is not about claiming certainty. It is about fostering a culture of shared responsibility, where users can understand not only what they are seeing, but how it was generated, discovered, or prioritized. By championing openness across the broader AI ecosystem, libraries can help communities navigate complexity with greater confidence, agency, and care. Here is an example from CMU.
7. Create the Space for Sensemaking
More than repositories of information, libraries can become spaces for making meaning. They are among the few environments where students, scholars, and communities can come together to ask difficult questions across disciplines, identities, and domains. We can design programs, workshops, and spaces that support dialogue, storytelling, and collaborative exploration. What matters most is creating the conditions for inquiry to thrive.
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We cannot outpace AI, but we can offer something far more enduring: continuity, context, and connection.
In this post-truth, post-neutrality moment, research and academic libraries are not fading into irrelevance. They are re-emerging, as spaces where complexity is welcomed, where doubt is not dismissed, and where reality, however unstable, is still worth pursuing.
This vision builds on the work of David Lankes, whose thinking continues to inspire new ways of understanding the role of libraries in uncertain times. His IFLA scenario reminds us that the future of information is not merely technical. It is deeply human.
And so, with that, let’s commit to helping our communities ask better questions, together.
Image generated using AI
This abstract visualization explores uncertainty and interconnected knowledge. Inspired by the idea of an “uncertainty matrix.”
