Algorithms in the Stacks: Investigating automated, for-profit diversity audits in public libraries

TL;DR

This paper examines the adoption of automated diversity audits in U.S. public libraries, highlighting their benefits, limitations, and implications for library practices and community representation.

Contribution

It provides an empirical analysis of how commercial automated diversity audits are used in libraries, revealing their impacts and suggesting improvements.

Findings

Libraries find audits convenient and time-saving.

Audits often oversimplify complex identities.

Dependence on vendors increases infrastructural reliance.

Abstract

Algorithmic systems are increasingly being adopted by cultural heritage institutions like libraries. In this study, we investigate U.S. public libraries' adoption of one specific automated tool -- automated collection diversity audits -- which we see as an illuminating case study for broader trends. Typically developed and sold by commercial book distributors, automated diversity audits aim to evaluate how well library collections reflect demographic and thematic diversity. 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 commercial systems. 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 vendors. We conclude with 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.