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

This presentation reports on a case study investigating how Natural Language Processing technologies can support the measurement and evaluation of gender biased language in archival catalogs.  Working with English descriptions from the catalog metadata of the University of Edinburgh’s Archives, we created an annotated dataset and classification models that identify types of gender biases in the descriptions.  Though conducted with archival data, the case study holds relevance across Galleries, Libraries, Archives, and Museums (GLAM), particularly for institutions with catalog descriptions written in English.  In addition to bringing Natural Language Processing methods to Archives, we identified opportunities to bring Archival Science methods, such as Cultural Humility (Tai, 2021) and Feminist Standpoint Appraisal (Caswell, 2022), to Natural Language Processing.  Through this two-way disciplinary exchange, we demonstrate how Humanistic approaches to bias and uncertainty can upend legacies of gender-based oppression that most computational approaches to date uphold when working with data at scale.