This talk theorizes the wide array of ways that gender and sex interact with machine learning (ML) and the artificial intelligence technologies that rely on it. Some of these interactions are intentional; others are unintentional or even opposed to practitioners’ concerted efforts to remove gender bias. Some are born out of the allure of a seemingly simple variable that is aligned with the technical needs of ML. Often, gender lurks without invitation, because these methods mine data for associations, and gendered associations are ubiquitous. In a growing body of work, scholars are using ML to actively interrogate measurements and theoriesof gender and sex. ML brings with it new paradigms of quantitative reasoning that hold the potential to either rein scribe or revolutionize gender in both technical systems and scientific knowledge.
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