Intersectional AI Is Essential

Polyvocal, Multimodal, Experimental Methods to Save Artificial Intelligence

  • Sarah Ciston University of Southern California

Abstract

Artificial intelligence is quietly shaping social structures and private lives. Although it promises parity and efficiency, its computational processes mirror biases of existing power even as often-proprietary data practices and cultural perceptions of computational magic obscure those influences. However, intersectionality—which foregrounds an analysis of institutional power and incorporates queer, feminist, and critical race theories—can help to rethink artificial intelligence. An intersectional framework can be used to analyze the biases and problems built into existing artificial intelligence, as well as to uncover alternative ethics from its counter-histories. This paper calls for the application of intersectional strategies to artificial intelligence at every level, from data to design to implementation, from technologist to user. Drawing on intersectional theories, the research argues these strategies are polyvocal, multimodal, and experimental—suggesting that community-focused and artistic practices can help imagine AI’s intersectional possibilities and help begin to address its biases.
Published
2019-12-29
Section
xCoAx 2019 - Special Issue