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An implicit expectation of asking users to rate agents, such as an AIdecision-aid, is that they will use only relevant information -- ask them aboutan agent's benevolence, and they should consider whether or not it was kind.Behavioral science, however, suggests that people sometimes use irrelevantinformation. We identify an instance of this phenomenon, where users whoexperience better outcomes in a human-agent interaction systematically ratedthe agent as having better abilities, being more benevolent, and exhibitinggreater integrity in a post hoc assessment than users who experienced worseoutcome -- which were the result of their own behavior -- with the same agent.Our analyses suggest the need for augmentation of models so that they accountfor such biased perceptions as well as mechanisms so that agents can detect andeven actively work to correct this and similar biases of users.

My Actions Speak Louder Than Your Words: When User Behavior Predicts Their Beliefs about Agents' Attributes