  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.
