Algorithms in the Stacks: Investigating automated, for-profit collection diversity audits in public libraries (Poster 4)

How well does your collection reflect your diverse community? How can you meaningfully evaluate diversity in your collection at scale? This poster will explore the current landscape of diversity audits for public library collections, and you will leave with a clear understanding of the various tools and methods available, as well as their respective strengths, weaknesses, benefits, and risks.

This poster will explore our investigation of public libraries’ adoption of one specific automated tool—automated collection diversity audits, as well as practitioners who have implemented these tools and methods. We investigate how these audits function, whether library workers find them useful, and what is at stake when sensitive, normative decisions about representation are outsourced to automated systems designed by private vendors. Our analysis draws on an anonymous survey of U.S. public librarians (n = 99), interviews with 14 librarians, a sample of purchasing records, and vendor documentation. We find that many library workers view these tools as convenient, time-saving solutions for assessing and diversifying collections under real and increasing constraints. Yet at the same time, the audits often flatten complex identities into standardized categories, fail to reflect local community needs, and further entrench libraries’ infrastructural dependence on commercial vendors. We will share recommendations for improving collection diversity audits and reflect on the broader implications for public libraries operating at the intersection of AI adoption, escalating anti-DEI backlash, and politically motivated defunding.