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