Collaboration Across the Archival and Computational Sciences to Address Legacies of Gender Bias in Descriptive Metadata

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Lucy Havens, Rachel Hosker, Beatrice Alex, Benjamin Bach, Melissa Terras

Published: 2023, Last Modified: 02 Jan 2026DH 2023EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This presentation reports on a case study investigating how Natural Language Processing technologies can support the measurement and evaluation of gender bias in archival catalogs. Furthermore, we demonstrate how Humanistic approaches can upend legacies of gender-based oppression that most computational approaches to date uphold when working with data at scale.