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.

本文通过行为科学研究社会互动中的一种现象，发现用户可能会在代理人评估中使用无关信息。作者的分析表明需要改进模型以考虑此类偏见，同时代理人也可以通过相应机制来检测和纠正用户的这种偏见。

我的行动胜过你的言语：当用户行为预测他们对代理属性的信念

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

Most animals possess the ability to actuate a vast diversity of movements,
ostensibly constrained only by morphology and physics. In practice, however, a
frequent assumption in behavioral science is that most of an animal's
activities can be described in terms of a small set of stereotyped motifs. Here
we introduce a method for mapping the behavioral space of organisms, relying
only upon the underlying structure of postural movement data to organize and
classify behaviors. We find that six different drosophilid species each perform
a mix of non-stereotyped actions and over one hundred hierarchically-organized,
stereotyped behaviors. Moreover, we use this approach to compare these species'
behavioral spaces, systematically identifying subtle behavioral differences
between closely-related species.

通过昆虫的姿势运动数据研究动物的行为，发现六种果蝇物种每种都表现出非成规动作和超过一百个等级组织的定型行为，此方法可以比较这些物种行为空间并系统地确定近缘物种间微妙的行为差异。