After opening the black box: Meta-dehumanization matters in algorithm recommendation aversion

Abstract

Perceptions of algorithms as opaque, commonly referred to as the black box problem, can make people reluctant to accept a recommendation from an algorithm rather than a human. Interventions that enhance people's subjective understanding of algorithms have been shown to reduce this aversion. However, across four preregistered studies (N = 960), we found that in the online shopping context, after explaining the algorithm recommendation process (versus human recommendation), users felt dehumanized and thus averse to algorithms (Study 1). This effect persisted, regardless of the type of algorithm (i.e., conventional algorithms or large language models; Study 2) or recommended product (i.e., search or experience products; Study 3). Notably, considering large language models (versus conventional algorithms) as the recommendation agent (Study 2) and framing algorithm recommendation as consumer-serving (versus website-serving; Study 4) mitigated algorithm aversion caused by meta-dehumanization. Our findings contribute to ongoing discussions on algorithm transparency, enrich the literature on human–algorithm interaction, and provide practical insights for encouraging algorithm adoption.