Closing the Loop: Bridging Machine Learning (ML) Research and Library Systems
Cordell, Ryan
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https://hdl.handle.net/2142/121630
Description
Title
Closing the Loop: Bridging Machine Learning (ML) Research and Library Systems
Author(s)
Cordell, Ryan
Issue Date
2023
Keyword(s)
Linda C. Smith
Festschrift
machine learning
cultural material
library system
Date of Ingest
2023-12-20T04:06:20-06:00
Abstract
This article argues that if libraries are to take leadership in conversations about the ethics and application of machine learning (ML) to cultural materials, they must move beyond the "perpetual future tense" of most library ML proposals and experiments, narrowing the gap separating promises that ML will enhance discoverability for library materials and the library systems through which most users encounter those materials. Even as ML methods have grown more powerful, nuanced, and sophisticated, ambitious hopes that ML might help better identify and describe vast library collections have been largely unmet, at least from the perspective of library patrons, researchers, and students. To address this gap, the article argues that libraries and ML researchers should work together to develop iterative, experimental, and even speculative interfaces that allow users to explore collections through ML-derived patterns that can enhance library data while educating users about ML processes, decisions, and biases.
Publisher
Johns Hopkins University Press and the Illinois School of Information Sciences, University of Illinois at Urbana-Champaign
Series/Report Name or Number
Library Trends 71 (1). August 2022
ISSN
0024-2594
Type of Resource
text
Genre of Resource
article
Language
eng
DOI
https://doi.org/10.1353/lib.2023.0008
Copyright and License Information
Copyright 2023 Board of Trustees of the University of Illinois
Library Trends 71 (1) August 2022: Library and Information Science, Interdisciplinary Perspectives: A Festschrift in Honor of Linda C. Smith. Edited by Anita S. Coleman and Martha Kyrillidou.
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