Oxford Handbook of the Sociology of Machine Learning

This chapter discusses 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 against practitioners’ concerted efforts. 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 theories of gender and sex. ML brings with it new paradigms of quantitative reasoning that hold the potential to either reinscribe or revolutionize gender in both technical systems and scientific knowledge.

Lockhart, Jeffrey W., 2023. ‘Gender, Sex, and the Constraints of Machine Learning Methods‘, in Christian Borch, and Juan Pablo Pardo-Guerra (eds), The Oxford Handbook of the Sociology of Machine Learning. (Preprint)