POST: What happens to libraries and librarians when machines can read all the books? 1

In a post sharing the text of a talk given at the Harvard Library Leadership in a Digital Age program, Chris Bourg (MIT) asks, “What happens to libraries and librarians when machines can read all the books?

Bourg posits that machine learning could play a key role in the future development of digital libraries. She goes on to provide working definitions for artificial intelligence and machine learning, and lists common concerns of librarians: will “robots take our jobs” or replace human-to-human relationships that are at the heart of libraries? Will AI “re-inscribe & magnify existing systems of inequality and racism, sexism, homophobia and the like”?

First, we need to accept that AI and machine learning are becoming more prevalent in our daily lives, and in many learning and research contexts.

Then we have to think about what concerns around AI that libraries and librarians are maybe especially well-suited to addressing; like privacy, context, authority, and ensuring the data used to train AI is inclusive and diverse and of high quality.

Bourg discusses the need for librarians to engage with AI and machine learning, both because of the possibilities to enhance our collections and our work, and to have librarian and information professional voices present in developing uses of the technology in GLAM contexts.

Turning the initial question around, Bourg asks, “what would we do if librarians could read all the books?”:

If we really could absorb all the information in our collections and make some sense of it, what would we do? What could we do if we had the capacity to read all our books, and maybe all the books in our peer libraries, and derive patterns from them?

What would we do that we can’t do now? What would we do better that we already do?

Can thinking about AI and machine learning in that way help us conceive of ways to leverage the fact that machines actually can do that now?

Finally let’s talk about how machine learning and AI might change or be changing research; and how we might start to think about optimizing our libraries to support new kinds of research made possible by text & data mining, AI and machine learning.

dh+lib Review

This post was produced through a cooperation between Leigh Bonds, Camille Cooper, Lydia Herring-Harrington, Veronica Ikeshoji-Orlati, Christina Kamposiori, Heather Martin, Susan Powelson, Shilpa Rele, (Editors-at-large for the week), Caitlin Christian-Lamb (Editor for the week), and Caro Pinto, Roxanne Shirazi, and Patrick Williams (dh+lib Review Editors).

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