Intersectionality and Machine Learning: Relaxing Improbable Assumptions

• In Event: Methods for Race, Ethnicity and Politics

Abstract

Using the lens of methodological pluralism, we investigate the compatibility of machine learning and intersectionality.  Intersectional quantitative methods would require the researcher to proactively specify the interwoven relationship between race, gender, and class; particularly as it relates to the political experience of multiply marginalized groups. In certain machine learning algorithms, there are required assumptions of independence between predictors, which make these methods unsuited for specifying and understanding intersectional group-based heterogeneity. This poses a tension between machine learning methods and intersectional scholars. In this piece, we will further the literature on quantitative methods for the study of identity by proposing the utility of Bayesian Classifiers and K Nearest Neighbors with relaxed assumptions of independence. This piece finds that the operationalization of race-gender identities in machine learning varies by the algorithm.

Authors

• Melina Much, UC Irvine

• Ines Levin, University of California, Irvine