Closing the Loop: Bridging Machine Learning (ML) Research and Library Systems

Abstract

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.