ACRL recently published an updated version of their AI Competencies for Library Workers, a set of standards for library workers which covers areas including ethical concerns, use of AI, and evaluating AI outputs. This is a really interesting document to explore and the document itself, and the discussion surrounding it, give insights into the diverse range of opinions and reactions librarians are having towards AI. I’d like to start a brief series where I’ll consider different elements and aspects of this document. To be contrary, I’d like to go out of order and actually start with competency area 2: knowledge and understanding of AI. Partly because area 1, ethics, is going to be a bear to discuss, and partly because I already touched upon area 2 in a presentation a few months ago. This is a work smarter, not harder post for your enjoyment.

But this is all easier said than done. Something that I’ve been concerned about, and have written about before, is the black box nature of much of our technological ecosystem. These are systems that are opaque or otherwise obscure. With a black box system, you can see inputs and outputs, but you lack an understanding of the internal workings of a system. We’ve been discussing black boxes for years with social media algorithms or with the Google search algorithm. These proprietary algorithms hold a great deal of influence over our lives and shape what we see and experience online. But the companies controlling these platforms are often loathe to shed light on how these systems actually work. And we’re seeing the same thing with AI, where companies aren’t exactly forthcoming about how their chatbot products, for instance, work. This black box environment holds implications for researchers, the general public using these tools, and for educators trying to equip people with AI literacy skills. How can you encourage knowledge and understanding of AI when you can’t fully see or understand the inner workings of a genAI tool?

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