AI and Blackness: Toward Moving Beyond Bias and Representation

In this article, we argue that AI ethics must move beyond the concepts of race-based representation and bias, and toward 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.

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Work Title AI and Blackness: Toward Moving Beyond Bias and Representation
Access
Open Access
Creators
  1. Christopher Dancy
  2. P. Khalil Saucier
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. IEEE Transactions on Technology and Society
Publication Date March 1, 2022
Publisher Identifier (DOI)
  1. https://doi.org/10.1109/TTS.2021.3125998
Deposited November 16, 2024

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