An Exploration of Intersectionality in Technology 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 addressing equity concerns with respect to unary identities (e.g., women, Black people), little to no work has studied intersectionality to determine how we can provide equitable outcomes for overlapping social identities in data-driven tech;To motivate explicit considerations for intersectionality in data-driven solutions and provide data to support future endeavors, we designed a survey to learn the landscape of intersectional identities in tech, where they contribute data, and how marginalized populations feel about the impact technology has on their lives..We piloted our survey with six participants. One interesting insight was that in general participants interacted with platforms like GitHub, Stack Overflow, Slack, and YouTube often; we also found that with social platforms participants ages 18-24 interacted more with Instagram, and participants ages 25+ preferred Facebook..To further support intersectional data in data science, we also propose a tool that supports evening the distribution of marginalized identities in datasets by finding and integrating intersectional data points. Unlike other tools which for the same reasons alter or remove data, this tool uses recommendations based on missing identities within a user provided data set to search other datasets for data to be merged.. Our data thus far, collected from 12 respondents and composed mostly of white males, 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.