机器人行为不公的时机影响人对公平的判断。
Dynamic Fairness Perceptions in Human-Robot Interaction
- 通过用户实验研究不公行为发生时间对公平感知的影响。
- 早段不公行为比晚段更易引发负面公平感知。
- 受惠者是机器人时，公平感下降更明显，适合人机交互研究者参考。
人们非常在意机器人对其的公平对待。现有研究通常在人机交互结束后测量公平感知，但这种静态方法忽略了交互过程中感知可能随时间变化。为此，我们开展了一项2×2混合设计的用户研究（N=40），考察两个因素：不公平机器人行为的发生时机（早期或晚期）以及受益者（另一台机器人或参与者）。结果表明，公平判断并非固定不变，会随不公行为的时间而动态变化。此外，我们基于组织公正理论中的三个关键因素（福利降低、行为表现、道德越轨）来预测即时公平感知，发现‘福利降低’和‘道德越轨’因子比整体模型更具预测力。研究证实，不公行为会影响人们对群体关系与机器人信任的感知，为未来动态公平感知研究提供了方向。
原文摘要 · Abstract (English)
People deeply care about how fairly they are treated by robots. The established paradigm for probing fairness in Human-Robot Interaction (HRI) involves measuring the perception of the fairness of a robot at the conclusion of an interaction. However, such an approach is limited as interactions vary over time, potentially causing changes in fairness perceptions as well. To validate this idea, we conducted a 2x2 user study with a mixed design (N=40) where we investigated two factors: the timing of unfair robot actions (early or late in an interaction) and the beneficiary of those actions (either another robot or the participant). Our results show that fairness judgments are not static. They can shift based on the timing of unfair robot actions. Further, we explored using perceptions of three key factors (reduced welfare, conduct, and moral transgression) proposed by a Fairness Theory from Organizational Justice to predict momentary perceptions of fairness in our study. Interestingly, we found that the reduced welfare and moral transgression factors were better predictors than all factors together. Our findings reinforce the idea that unfair robot behavior can shape perceptions of group dynamics and trust towards a robot and pave the path to future research directions on moment-to-moment fairness perceptions
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。