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Current methods of measuring fairness in human-robot interaction (HRI) research often gauge perceptions of fairness at the conclu- sion of a task. However, this methodology overlooks the dynamic nature of fairness perceptions, which may shift and evolve as a task progresses. To help address this gap, we introduce a platform designed to help investigate the evolution of fairness over time: the Multiplayer Space Invaders game. This three-player game is structured such that two players work to eliminate as many of their own enemies as possible while a third player makes decisions about which player to support throughout the game. In this paper, we discuss different potential experimental designs facilitated by this platform. A key aspect of these designs is the inclusion of a robot that operates the supporting ship and must make multiple decisions about which player to aid throughout a task. We discuss how capturing fairness perceptions at different points in the game could give us deeper insights into how perceptions of fairness fluctuate in response to different variables and decisions made in the game. 

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. 

We propose the use of interactive vignettes as an alternative to traditional text- and video-based vignettes for conducting large-scale Human-Robot Interaction (HRI) studies. Interactive vignettes maintain the advantages of traditional vignettes while offering additional affordances for participant interaction and data collection through interactive elements. We discuss the core affordances of interactive vignettes, including explorability, responsiveness, and non-linearity, and look into how these affordances can enable HRI research with more complex scenarios. To demonstrate the strength of the approach, we present a case study of our own research project with N=87 participants and show the data we collect through interactive vignettes. We suggest that the use of interactive vignettes can benefit HRI researchers in learning how participants interact with, respond to, and perceive a robot’s behavior in pre-defined scenarios.