Averse to what: Consumer aversion to algorithmic labels, but not their outputs?

Abstract

Inspired by significant technical advancements, a rapidly growing stream of research explores human lay beliefs and reactions surrounding AI tools, which employ algorithms to mimic elements of human intelligence. This literature predominantly documents negative reactions to these tools or the underlying algorithms, often referred to as algorithm aversion or, alternatively, a preference for humans. This article proposes a third interpretation: people may be averse to their labels, but appreciative of their output. This perspective offers three core insights for how we study people's reactions to algorithms. Research would benefit from (1) carefully considering the labeling of AI tools, (2) broadening the scope of study to include interactions with these tools, and (3) accounting for their technical configuration.