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

2025-05-20

AI Summary

This study examines U.S. public libraries’ adoption of commercial automation tools for collection diversity audits, addressing critical gaps in assessing demographic and topical representation, staff-perceived utility, and the ethical–structural risks of outsourcing normative representational decisions to for-profit algorithms. Employing a mixed-methods approach—including an anonymous survey (n=99), in-depth interviews (n=14), procurement record analysis, and critical discourse analysis of vendor documentation—the study reveals, for the first time, that such tools systematically oversimplify identity dimensions, decontextualize community-specific needs, and reinforce technical dependency on publishers. While enhancing audit efficiency, they suffer from fundamental flaws: rigid identity categorizations and poor local adaptability. The findings underscore governance vulnerabilities in AI-driven DEI initiatives under political and fiscal pressure. The study proposes design principles prioritizing equity, contextual sensitivity, and technological sovereignty.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Investigating automated diversity audits in public libraries
• Assessing usefulness and impact of commercial algorithmic tools
• Exploring risks of outsourcing representation decisions to vendors

Innovation

• Automated diversity audits evaluate library collections
• Commercial tools simplify but flatten identity complexity
• Survey and interviews reveal vendor dependence issues