AI, Normality, and Oppressive Things (Extended Abstract)

While it is well-known that AI systems might bring about unfair social impacts by influencing social schemas, much attention has been paid to instances where the content presented by AI systems explicitly demeans marginalized groups or reinforces problematic stereotypes. This paper urges critical scrutiny to be paid to instances that shape social schemas through subtler manners. Drawing from recent philosophical discussions on the politics of artifacts, we argue that many existing AI systems should be identified as what Liao and Huebner called oppressive things when they function to manifest oppressive normality. We first categorize three different ways that AI systems could function to manifest oppressive normality and argue that those seemingly innocuous or even beneficial for the oppressed group might still be oppressive. Even though oppressiveness is a matter of degree, we further identify three features of AI systems that make their oppressive impacts more concerning. We end by discussing potential responses to oppressive AI systems and urge remedies that go beyond fixing the unjust outcomes but also challenge the unjust power hierarchies of oppression.