An Exploration of Intersectionality in Software Development and Use

Abstract

The growing ubiquity of machine learning technologies has led to concern and concentrated efforts at improving data-centric research and practice. While much work has been done on addressing equity concerns with respect to unary identities (e.g, race or gender), little to no work in Software Engineering has studied intersectionality to determine how we can provide equitable outcomes for complex, overlapping social identities in data-driven tech. To this end, we designed a survey to learn the landscape of intersectional identities in tech, where these populations contribute data, and how marginalized populations feel about the impact technology has on their day to day lives. Our data thus far, collected from 12 respondents and composed mostly of white and male identities, further highlights the lack of representation in modern data sets and need for contributions that explicitly explore how to support data-driven research and development. ACM Reference Format: Hana Winchester, Alicia E. Boyd, and Brittany Johnson. 2022. An Exploration of Intersectionality in Software Development and Use. In Third Workshop on Gender Equality, Diversity, and Inclusion in Software Engineering (GE@ICSE'22), May 20, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3524501.3527605