The social consequences of Machine Allocation Behavior: Fairness, interpersonal perceptions and performance

• Published in: Computers in human behavior Vol. 146; p. 107628
• Main Authors: Claure, Houston, Kim, Seyun, Kizilcec, René F., Jung, Malte
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2023
• Subjects: Artificial intelligence; Fairness in artificial intelligence; Group dynamics; Perceived fairness; Group dynamics; Artificial intelligence; Fairness in artificial intelligence; Perceived fairness
• ISSN: 0747-5632; 1873-7692
• DOI: 10.1016/j.chb.2022.107628

Machines increasingly decide over the allocation of resources or tasks among people resulting in what we call Machine Allocation Behavior. People respond strongly to how other people or machines allocate resources. However, the implications for human relationships of algorithmic allocations of, for example, tasks among crowd workers, annual bonuses among employees, or a robot’s gaze among members of a group entering a store remains unclear. We leverage a novel research paradigm to study the impact of machine allocation behavior on fairness perceptions, interpersonal perceptions, and individual performance. In a 2 × 3 between-subject design that manipulates how the allocation agent is presented (human vs. artificial intelligent [AI] system) and the allocation type (receiving less vs. equal vs. more resources), we find that group members who receive more resources perceive their counterpart as less dominant when the allocation originates from an AI as opposed to a human. Our findings have implications on our understanding of the impact of machine allocation behavior on interpersonal dynamics and on the way in which we understand human responses towards this type of machine behavior. •Receiving more resources from an AI shapes how dominant group members are perceived.•Collaborative Tetris is an effective platform for exploring fairness in groups.•Fairness is better understood as a dynamic phenomenon that develops over time.•A machine’s allocation behavior is crucial to understanding its impact on groups.