Racial Disregard in Algorithmic Fairness

The realization that algorithms can perpetuate or exacerbate racial disparities in society has spurred significant research in the field of algorithmic fairness. Concisely, contending with racism has been a primary motivation and driver of research in this area. Though racism is a primary motivation, developing strategies to correct and/or prevent racist outcomes is an ongoing challenge in the field. In particular, racism tends to be concealed by seemingly “race-neutral” methods and rhetoric making it difficult to identify. How do we solve a problem that we cannot see? Scholars refer to this modern form of racism as colorblind racism which occurs when we observe a racially discriminatory outcome but the mechanism responsible for said outcome appears to have nothing to do with race. In this paper, we introduce the concept of racial disregard in algorithmic fairness. The three components are i) disregard for racial context or racial issues ii) disregard for lived experience and perspectives of minoritized people iii) disregard for ongoing harms caused by a legacy of discrimination. With this definition in hand, we explore racial disregard within existing algorithmic fairness research. We discuss how this more nuanced form of racism can enhance the ongoing research agenda in the field. Understanding racial disregard is crucial for addressing the racial disparities that have motivated much of the algorithmic fairness research agenda. Furthermore, recognizing the invisibility of racial disregard is essential in developing effective solutions to combat racial bias in algorithms. Ultimately, our simple conceptual framework helps identify occurrences of racial disregard to promote regard by attending to racism more directly instead of avoiding a main motivator and driver of research.