Unfair! Perceptions of Fairness in Human-Robot Teams

Abstract

How team members are treated influences their performance in the team and their desire to be a part of the team in the future. Fairness metrics that properly capture people's perception of fairness in human-robot teaming remains a research gap. We present work on assessing how well objective metrics capture people's perception of fairness. We show that these metrics align with perceived fairness. Importantly, we discover that there are bleed-over effects in people's assessment of fairness.