AI and Blackness: Towards moving beyond bias and representation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
EthicsSimilar Papers 제목 키워드 기반
Using a Cognitive Architecture to consider antiblackness in design and development of AI systems
How might we use cognitive modeling to consider the ways in which antiblackness, and racism more broadly, impact the design and development of AI systems? We provide a discussion and an example towards an answer to this …
Cultural Endowment as Collective Improvisation: subjectivity and digital infinity
Philosophically, a repertoire of signifying practices as constitutive of a cultural endowment was said to be ambiguous or unworthy of pursuit. Currently, a unique capacity of the mind is considered to be its ability to p…
Identifying Potential Inlets of Man in the Artificial Intelligence Development Process
In this paper we hope to identify how the typical or standard artificial intelligence development process encourages or facilitates the creation of racialized technologies. We begin by understanding Sylvia Wynter's defin…
ManagementResampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes
We tackle societal bias in image-text datasets by removing spurious correlations between protected groups and image attributes. Traditional methods only target labeled attributes, ignoring biases from unlabeled ones. Usi…
Image Captioningimage-classificationImage ClassificationMulti-Label Image ClassificationOn Evaluating and Mitigating Gender Biases in Multilingual Settings
While understanding and removing gender biases in language models has been a long-standing problem in Natural Language Processing, prior research work has primarily been limited to English. In this work, we investigate s…