Measuring Intersectional Biases in Historical Documents

Nadav Borenstein*, Karolina Stanczak*, Thea Rolskov, Natália da Silva Perez, Natacha Klein Kafer, Isabelle Augenstein

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

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Abstract

Data-driven analyses of biases in historical texts can help illuminate the origin and development of biases prevailing in modern society. However, digitised historical documents pose a challenge for NLP practitioners as these corpora suffer from errors introduced by optical character recognition (OCR) and are written in an archaic language. In this paper, we investigate the continuities and transformations of bias in historical newspapers published in the Caribbean during the colonial era (18th to 19th centuries). Our analyses are performed along the axes of gender, race, and their intersection. We examine these biases by conducting a temporal study in which we measure the development of lexical associations using distributional semantics models and word embeddings. Further, we evaluate the effectiveness of techniques designed to process OCR-generated data and assess their stability when trained on and applied to the noisy historical newspapers. We find that there is a trade-off between the stability of the word embeddings and their compatibility with the historical dataset. We provide evidence that gender and racial biases are interdependent, and their intersection triggers distinct effects. These findings align with the theory of intersectionality, which stresses that biases affecting people with multiple marginalised identities compound to more than the sum of their constituents.
Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: ACL 2023
Place of PublicationToronto, Canada
Pages2711-2730
Number of pages20
DOIs
Publication statusPublished - 14 Jul 2023

Publication series

SeriesFindings of the Association for Computational Linguistics

Bibliographical note

©2023 Association for Computational Linguistics

Research programs

  • ESHCC HIS

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