Mixing It Up: How Mixed Groups of Humans and Machines Modulate Conformity

Abstract

This study examines to what extent mixed groups of computers and humans are able to produce conformity effects in human interaction partners. Previous studies reveal that nonhuman groups can induce conformity under certain circumstances, but it is unknown to what extent mixed groups of human and nonhuman agents are able to produce similar effects. It is also unknown how varying the number of human agents per group can affect conformity. Participants were assigned to one of five groups varying in their proportion of human to nonhuman agent composition and were asked to complete a social and analytical task with the assigned group. These task types were chosen to represent tasks which humans (i.e., social task) or computers (i.e., analytical task) may be perceived as having greater expertise in, as well as roughly approximating real-world tasks humans may complete. A mixed analysis of variance (ANOVA) revealed higher rates of conformity (i.e., percentage of time participants answered in line with their group on critical trials) with the group opinion for the analytical versus the social task. In addition, there was an impact of the ratio of human to nonhuman agents per group on conformity on the social task, with higher conformity with the group opinion as the number of humans in the group increased. No such effect was observed for the analytical task. The findings suggest that mixed groups produce different levels of conformity depending on group composition and task type. Designers of systems should be aware that group composition and task type may influence compliance and should design systems accordingly.