Being a subject selector in an academic library is pretty repetitive. I’m basically applying the same selection criteria to different materials over and over again. In my specific role, I’m almost always looking for books (and ebooks) that are for lower-division undergraduates/general readers; that are from reputable academic presses; and that fall within the subject in question. I have some other more subtle requirements that I won’t attempt to explain here, but the point is that I’m applying these same criteria every single time.

Perhaps we could train a machine learning model to do this work. If it watched me select books long enough, I bet such a model could even pick up on the finer nuances by which I’m choosing titles. It could then probably do a great job of applying those criteria. The relatively constrained nature of the problem might make this a feasible problem to solve.

Librarians would probably want to train the model with their own preferences for their subject areas. A human-in-the-loop approach might be sensible. Maybe the team at ProQuest’s Oasis (for example) would be willing to build us such a feature?

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