The San José State University School of Information wanted to have a half-course on artificial intelligence in their portfolio, and asked me to develop and teach it. (Thanks!) So I got a blank canvas on which to paint eight weeks of…whatever you might want graduate students in library & information science students to know about AI.
For those of you who just want the reading list, here you go. For those of you who thought about the second-to-last sentence: ahahaha.
This is of course the problem of all teachers — too much material, too little time — and in an iSchool it’s further complicated because, while many students have technological interests and expertise, few have programming skills and even fewer have mathematical backgrounds, so this course can’t be “intro to programming neural nets”. I can gesture in the direction of linear algebra and high-dimensional spaces, but I have to translate it all into human English first.
But further, even if I were to do that, it wouldn’t be the right course! As future librarians, very few of my students will be programming neural nets. They are much more likely to be helping students find sources for papers, or helping researchers find or manage data sets, or supporting professors who are developing classes, helping patrons make sense of issues in the news, and evaluating vendor pitches about AI products. Which means I don’t need people who can write neural net code; I need people who understand the basics of how machine learning operates, who can do some critical analysis, situate it in its social context. People who know some things about what data is good for, how it’s hard, where to find it. People who know at least the general direction in which they might find news articles and papers and conferences that their patrons will care about. People who won’t be too dazzled by product hype and can ask pointed questions about how products really work, and whether they respect library values. And, while we’re at it, people who have some sense of what AI can do, not just theoretically, but concretely in real-world library settings.
Eight weeks: go!
What I ended up doing was 4 2-week modules, with a rough alternation of theory and library case studies, and a pretty wild mix of readings: conference presentations, scholarly papers from a variety of disciplines, hilarious computational misadventures, news articles, data visualizations. I mostly kept a lid on the really technical stuff in the required readings, but tossed a lot of it into optional readings, so that students with that background or interest could pull on those threads. (And heavily annotated the optional readings, to give people a sense of what might interest them; I’d like to say this is why surprisingly many of my students did some optional reading, but actually they’re just awesome.) For case studies, we looked at the Northern Illinois University dime novels collection experiments; metadata enrichment in the Charles Teenie Harris archive; my own work with HAMLET; and the University of Rhode Island AI lab. This let us hit a gratifyingly wide variety of machine learning techniques, use cases (metadata, discovery, public services), and settings (libraries, archives).
Do I have a couple of pages of things to change up next time I teach the class (this fall)? Of course I do. But I think it went well for a first-time class (particularly for a first-time class in the middle of a global catastrophe…)
Big ups to the following:
- Matthew Short of NIU and Bohyun Kim of URI, for guest speaking;
- Everyone at SJSU who worked on their “how to teach online” materials, especially Debbie Faires — their onboarding did a good job of conveying SJSU-specific expectations and building a toolkit for teaching specifically online in a way that was useful to me as someone with a lot of offline teaching experience;
- Zeynep Tufecki, Momin Malik, Catherine D’Ignazio, who suggested readings that I ended up assigning;
- and my students, who are about to get a paragraph.
My students. Look. You signed up to take a class online — it’s an all-online program — but none of you signed up to do it while being furloughed, while homeschooling, while being sick with a scary new virus. And you knocked it out of the park. Week after week, asking for the smallest of extensions to hold it all together, breaking my heart in private messages, while publicly writing thoughtful, well-researched, footnoted discussion posts. While not only doing even the optional readings, but finding astonishment and joy in them. While piecing together the big ideas about data and bias and fairness and the genuine alienness of machine intelligence. I know for certain, not as an article of faith but as a statement of fact, that I will keep seeing your names out there, that your careers will go places, and I hope I am lucky enough to meet you in person someday.