Task-Dependent Algorithm Aversion

TLDR

Consumers are averse to relying on algorithms for tasks typically performed by humans, even though algorithms often outperform humans. The authors investigate when and why this aversion occurs across diverse domains. They conducted four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. They found that algorithms are trusted less for subjective tasks, but that boosting perceived objectivity or affective human‑likeness increases trust and use, revealing that consumers mistakenly believe algorithms lack the abilities for subjective tasks and that marketers can leverage these insights to promote algorithm adoption when they outperform humans.

Abstract

Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task’s perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms’ perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.