Intersectionality identifies multiple interdependent factors of advantage and disadvantage. It refers to the interconnected nature of social categorizations such as race, class, and gender as they apply to a given individual or group
Black women workers at GM were told they had no legal grounds for a discrimination case against their employer because antidiscrimination law only protected single-identity categories.
The concept of intersectionality provided the grounds for a long, slow paradigm shift that is still unfolding in the social sciences, in legal scholarship, and in other domains of research and practice.
While there is rapidly growing interest in algorithmic bias audits, especially in the fairness, accountability, and transparency in machine learning community, most are single-axis: they look for a biased distribution of error rates only according to a single variable, such as race or gender.
One question about [intersectional approaches] is how many identity variables to include because each adds complexity (and, in many situations, time and cost) to audits.