Gender, Sex, and the Constraints of Machine Learning Methods

Jeffrey W Lockhart
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Jeffrey W Lockhart: University of Chicago

No zj468, SocArXiv from Center for Open Science

Abstract: Machine learning interacts with gender and sex in myriad ways, intentionally, unintentionally, and sometimes even against practitioner's concerted efforts. Some of these interactions are born out of the allure of a seemingly simple, unambiguous, binary, variable ideally aligned with the technical needs and sensibilities of ML. Most of the time, gender lurks in ML systems without any explicit invitation, simply because these systems mine data for associations, and gendered associations are ubiquitous. And in a growing body of work, scholars are using ML to actively interrogate gender and sexuality, in turn shaping what they mean and how we think about them. Machine learning brings with it new paradigms of quantitative reasoning which hold the potential to either reinscribe or revolutionize gender in not only technical systems, but scientific knowledge as well. Throughout, the key is for people in and around machine learning to pay close attention to what the technology is actually doing with gender and sex.

Date: 2022-11-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-gen
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:zj468

DOI: 10.31219/osf.io/zj468

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