An Intersectional Definition of Fairness

With the rising influence of machine learning algorithms on many important aspects of our daily lives, there are growing concerns that biases inherent in data can lead the behavior of these algorithms to discriminate against certain populations [1, 2, 4, 6, 8, 28, 29, 15]. In recent years, substantial research effort has been devoted to the development of mathematical definitions of bias, or its opposite, fairness, in algorithms and in data [15, 18, 26, 23, 19, 32]. In this work, we focus on the fairness scenario where there are multiple protected attributes that we aim to ensure fairness for, and which may potentially overlap with each other, such as gender, race, and sexual orientation. Our guiding principle is intersectionality, the core theoretical framework underlying the thirdwave feminist movement [13]. The principle of intersectionality states that racism, sexism, and other social systems which harm marginalized groups are interlocking in their effects, such that the lived experience of, e.g., black women, is very different than that of, e.g., white women. Intersectionality was defined by Kimberlé Crenshaw in the 1980's [13] and popularized in the 1990's, e.g. by Patricia Hill Collins [10], although the ideas are much older [11, 35]. In the context of machine learning and fairness, intersectionality was recently considered by [7], who studied the impact of the intersection of gender and skin color on computer vision performance, and by [23, 19], who aimed to protect certain subgroups in order to prevent "fairness gerrymandering."