Is this AI sexist? The effects of a biased AI’s anthropomorphic appearance and explainability on users’ bias perceptions and trust

Abstract

Biases in artificial intelligence (AI), a pressing issue in human-AI interaction, can be exacerbated by AI systems’ opaqueness. This paper reports on our development of a user-centered explainable-AI approach to reducing such opaqueness, guided by the theoretical framework of anthropomorphism and the results of two 3 × 3 between-subjects experiments (n = 207 and n = 223). Specifically, those experiments investigated how, in a gender-biased hiring situation, three levels of AI human-likeness (low, medium, high) and three levels of richness of AI explanation (none, lean, rich) influenced users’ 1) perceptions of AI bias and 2) adoption of AI’s recommendations, as well as how such perceptions and adoption varied across participant characteristics such as gender and pre-existing trust in AI. We found that comprehensive explanations helped users to recognize AI bias and mitigate its influence, and that this effect was particularly pronounced among females in a scenario where females were being discriminated against. Follow-up interviews corroborated our quantitative findings. These results can usefully inform explainable AI interface design.