AI and Blackness: Towards moving beyond bias and representation
This work addresses the problem of systemic antiblackness in AI for researchers and practitioners, proposing a more foundational critique beyond incremental bias fixes.
The paper argues that AI ethics should shift from focusing on racial bias and representation to examining the deeper ontological foundations of AI systems, particularly anti-Black racism, and uses an audit of ConceptNet to illustrate antiblackness despite debiasing efforts.
In this paper, we argue that AI ethics must move beyond the concepts of race-based representation and bias, and towards those that probe the deeper relations that impact how these systems are designed, developed, and deployed. Many recent discussions on ethical considerations of bias in AI systems have centered on racial bias. We contend that antiblackness in AI requires more of an examination of the ontological space that provides a foundation for the design, development, and deployment of AI systems. We examine what this contention means from the perspective of the sociocultural context in which AI systems are designed, developed, and deployed and focus on intersections with anti-Black racism (antiblackness). To bring these multiple perspectives together and show an example of antiblackness in the face of attempts at de-biasing, we discuss results from auditing an existing open-source semantic network (ConceptNet). We use this discussion to further contextualize antiblackness in design, development, and deployment of AI systems and suggest questions one may ask when attempting to combat antiblackness in AI systems.