Paper Number
ECIS2026-1666
Paper Type
CRP
Abstract
When robots provide services to people, issues of fairness can arise, which in turn affect how humans perceive the robot. From the fields of explainable artificial intelligence (XAI), we know that explanations about the system behaviour can influence how humans perceive a system. However, research in this field indicates that this impact is context-dependent, depending on factors such as the type of explanation, the nature of the actors' goals, or the degree of (un)fairness. In this paper, we investigate how verbal rational explanations of a service robot influence how humans perceive fairness and the robot as such. We look into situations in which a robot provides services to competing people in an unfair manner. By conducting an experiment (n = 44), we demonstrate that verbal explanations can draw attention to the cause of injustice; however, they cannot compensate for the more negative perception of a robot due to personal disadvantage.
Recommended Citation
Büttner, Sebastian Thomas; Gutzmann, Jan Christoph; Neumann, Paul; Beşer, Alper; and Prilla, Michael, ""Something Is Unfair. Can You Explain Why?" – Implications Of Verbal Explanations On Perceived Fairness Of Service Robots" (2026). ECIS 2026 Proceedings. 11.
https://aisel.aisnet.org/ecis2026/cog_hbis/cog_hbis/11
"Something Is Unfair. Can You Explain Why?" – Implications Of Verbal Explanations On Perceived Fairness Of Service Robots
When robots provide services to people, issues of fairness can arise, which in turn affect how humans perceive the robot. From the fields of explainable artificial intelligence (XAI), we know that explanations about the system behaviour can influence how humans perceive a system. However, research in this field indicates that this impact is context-dependent, depending on factors such as the type of explanation, the nature of the actors' goals, or the degree of (un)fairness. In this paper, we investigate how verbal rational explanations of a service robot influence how humans perceive fairness and the robot as such. We look into situations in which a robot provides services to competing people in an unfair manner. By conducting an experiment (n = 44), we demonstrate that verbal explanations can draw attention to the cause of injustice; however, they cannot compensate for the more negative perception of a robot due to personal disadvantage.
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