Managing Bias When Library Collections Become Data

Everett-Hayes, Lauren

Coleman, C. N. (2020). Managing bias when library collections become data. International Journal of Librarianship, 5(1), 8-19. https://doi.org/10.23974/ijol.2020.vol5.1.162

Summary

Catherine Coleman’s article addresses AI developments and how libraries should be critical of their integration into library functions, instead focusing on the ethos of what libraries do for patrons. For one, the point is brought up that AI has shown to be biased because of the nature of how it gets its data. It can only take what it can access, which is not a complete view of the information someone may try to access. The author illustrates that a collection is where library data is concentrated, and, when considering how to incorporate AI, how you access information becomes extremely more relevant (Coleman, 2020).

A core thing the author seemed to want to get across was that AI should be wielded as a tool instead of as a solution as it could help with assessing bias in collections. A current example of overarching bias are the paradoxical LOC subject headings in how they are necessary for categorizing but also cause problems through misrepresentation, racism, etc. The author also expertly speaks about how libraries are more relevant than ever with the integration of AI:

At this moment when there are as many papers about the successes of AI research as there are papers calling out algorithmic bias, data bias, and setting forth principles of AI practice, libraries need to do much more than provide curated data to AI researchers. Libraries need to apply the principles of the profession to managing bias in AI-based systems. (Coleman, 2020, p. 16)

Lastly, Coleman (2020) sums up the article perfectly with the following quote, which pairs well with her call to action that is illustrated throughout: “Libraries need what AI has to offer, but AI needs what librarians have to offer even more” (p. 16).

Evaluation

Overall, I found this article well articulated and felt that it pushed the subject on AI in libraries in effective ways that both explored how AI could help and where it has limitations. Unsurprisingly, there are a lot of things librarians have to consider regarding our library Code of Ethics, copyright, and accessibility to our collections as there is more of a push to use AI and allow it to access our collections without a leash. However, this article talks about how AI can be both a barrier and an asset that continuously needs human input and discretion. In this back-and-forth, there ends up being a lot of really great questions and points made by Coleman that gives us a lot to think about and prepare for, but also feel empowered by. I don't think we have to be afraid of AI replacing our work or making libraries obsolete. The way in which humans understand other humans and what they need will always exist through the nature of research and making connections to the scholarly conversations out there. AI might get robust enough to aid us in seeing our biases and analyzing collections on a deeper level, but I don't think we will use it to put the books on the shelf, since human patrons will always be our focus.

As an honorable mention that might be interesting to others, there is an open access book that is mentioned in this article that gives a humanistic perspective on data that I would like to note here: All Data Are Local: Thinking Critically in a Data-Driven Society

Rethinking Libraries for the Age of AI

 Slick, Becca

Sousa, N. M. T. (2025). Academic libraries as hubs of artificial intelligence competency. Discover Artificial Intelligence, 5(1), 221. https://doi.org/10.1007/s44163-025-00490-8 

    Artificial intelligence (AI) is everywhere right now, whether we’re using it for research, writing, or just everyday tasks. But in the article Academic Libraries as Hubs of Artificial Intelligence Competency, the author argues that libraries need to do more than just provide access to these tools. They should actually help people understand how AI works and how it shapes the information we rely on.

    For a long time, libraries have been seen as neutral spaces, places where information is organized and made available without bias. But this article pushes back on that idea. It argues that neutrality isn’t really possible anymore, especially when so much of the information we access is filtered through algorithms. Search engines, databases, and recommendation systems all play a role in deciding what we see (and what we don’t). And those systems aren’t neutral at all, they’re built by people and influenced by things like data choices, commercial interests, and existing biases.

    Because of this, the author says libraries need to step up and take a more active role.

    One of the main ideas in the article is AI literacy. Basically, this means helping people understand not just how to use AI tools, but how to think about them. That includes knowing how AI systems work (at least at a basic level), recognizing their limitations, and being able to question the results they produce. It’s not just about getting help on an assignment, it’s really about understanding how AI is shaping knowledge and decision-making in everyday life.

    This is where libraries come in. The article argues that libraries are actually in a great position to teach these skills. Instead of just helping students find sources, libraries could:

• show how algorithms influence search results
• talk about bias in AI systems
• encourage more critical thinking about information
• and highlight voices that might otherwise be overlooked

    In other words, libraries can become places where people learn how to navigate a world that’s increasingly run by AI.

    Of course, the article also points out that this isn’t easy. Libraries are dealing with budget cuts, reliance on commercial databases, and long-standing expectations to stay “neutral.” On top of that, not all librarians have training in AI, feel prepared to teach it, or refuse to learn about it altogether. But even with these challenges, the author argues that libraries have an important opportunity to evolve and stay relevant.

    At the end of the day, the article makes a pretty clear point: libraries can’t just be about access anymore, they need to be about understanding. In a world shaped by AI, that shift matters more than ever. 

Evaluation - I think this article brings up a really important issue, especially for those in an MLIS program. AI isn’t going away, and a lot of students are already using it without really thinking about how it works or what its limitations are. So the idea that libraries could help fill that gap makes a lot of sense.

    What stood out to me most was the argument about neutrality. Libraries have traditionally tried to stay neutral, but this article makes a good case that neutrality can actually be a problem. If we don’t question the systems we’re using, especially algorithm-driven ones, we’re basically just accepting whatever they give us. That’s not great, especially when bias and misinformation are real concerns.

    That said, the article is definitely a bit heavy in terms of language. Some of the wording (like “epistemic” concepts) makes it harder to read than it needs to be. I found myself having to slow down and reread parts, which might be a barrier for a broader audience. It also stays pretty theoretical, there aren’t a lot of concrete examples of libraries already doing this work, which would’ve helped make the ideas feel more practical.

    Overall though, I think the message is solid. Libraries have always been about supporting learning and access to knowledge, and this feels like a natural next step. If anything, this article is a reminder that our role as future librarians might be a lot more active, and a lot more important, than just managing collections.