Comparing discriminatory behavior against AI and humans

Abstract

Abstract Although discrimination is typically believed to occur from well-defined categories like ethnicity, disability, and sex, studies have found that discrimination persists in minimal conditions lacking such categories. Participants have been found to preferentially allocate resources based on seemingly arbitrary shared characteristics such as dot estimation choices. Here, we use a preregistered experiment ( n  = 500) to investigate whether humans discriminate in a similar manner when interacting with artificial intelligence (AI) agents that ostensibly made dot estimations. We hypothesized that because humans harbor prejudice against algorithms relative to other humans (otherwise known as algorithm aversion), the strength of discriminatory behavior may be greater against AI than humans. Surprisingly, we found that participants distributed resources in a similar manner, albeit unequally, to both human and AI agents. Specifically, participants favored the other agent when decisions were aligned. Our findings suggest that discriminatory behavior is less influenced by the recipient’s identity and more shaped by choice congruency.