Don’t be unfair, Mr Bot! : An empirical study exploring the perception of fairness in non-work settings for human-agent interactions

This study aimed to explore the implementation of fairness in intelligent agents to enhance their interactions in our social space. Two distinct investigations, an experiment, and a focus group, were conducted to examine the impact of unfair treatment by non-anthropomorphic and anthropomorphic agents, where we sought to answer the research question: How does experiencing unfair treatment from agents with different appearances influence individuals' perceptions, satisfaction, and trust? The experiment encompassed four experimental conditions combining fair and unfair behaviours with agents displaying human-like or non-human-like appearances. User enactment, Experience prototyping, and the Wizard of Oz technique were employed during the experiment. The focus group aimed to delve into the concept of fairness and its relevance to agents in greater detail. In summary, the study's findings indicate that fairness is a significantly important consideration in agent design. However, the complexity of designing a fair agent proves challenging, due to the subjective and contextual nature where it entangles with various factors.Toward socially competent AI: Designing multi-user interaction with embodied intelligent agents to support politeness and fairness (SCAI