Algorithmic Mediation in Group Decisions

Abstract

How do individuals perceive algorithmic vs. group-made decisions? We investigated people's perceptions of mathematically-proven fair division algorithms making social division decisions. In our first qualitative study, about one third of the participants perceived algorithmic decisions as less than fair (30% for self, 36% for group), often because algorithmic assumptions about users did not account for multiple concepts of fairness or social behaviors, and the process of quantifying preferences through interfaces was prone to error. In our second experiment, algorithmic decisions were perceived to be less fair than discussion-based decisions, dependent on participants' interpersonal power and computer programming knowledge. Our work suggests that for algorithmic mediation to be fair, algorithms and their interfaces should account for social and altruistic behaviors that may be difficult to define in mathematical terms.