  Research in social psychology has shown that people's biased, subjective
judgments about another's personality based solely on their appearance are not
predictive of their actual personality traits. But researchers and companies
often utilize computer vision models to predict similarly subjective
personality attributes such as "employability." We seek to determine whether
state-of-the-art, black box face processing technology can learn human-like
appearance biases. With features extracted with FaceNet, a widely used face
recognition framework, we train a transfer learning model on human subjects'
first impressions of personality traits in other faces as measured by social
psychologists. We find that features extracted with FaceNet can be used to
predict human appearance bias scores for deliberately manipulated faces but not
for randomly generated faces scored by humans. Additionally, in contrast to
work with human biases in social psychology, the model does not find a
significant signal correlating politicians' vote shares with perceived
competence bias. With Local Interpretable Model-Agnostic Explanations (LIME),
we provide several explanations for this discrepancy. Our results suggest that
some signals of appearance bias documented in social psychology are not
embedded by the machine learning techniques we investigate. We shed light on
the ways in which appearance bias could be embedded in face processing
technology and cast further doubt on the practice of predicting subjective
traits based on appearances.
