  Personality perception is implicitly biased due to many subjective factors,
such as cultural, social, contextual, gender and appearance. Approaches
developed for automatic personality perception are not expected to predict the
real personality of the target, but the personality external observers
attributed to it. Hence, they have to deal with human bias, inherently
transferred to the training data. However, bias analysis in personality
computing is an almost unexplored area. In this work, we study different
possible sources of bias affecting personality perception, including emotions
from facial expressions, attractiveness, age, gender, and ethnicity, as well as
their influence on prediction ability for apparent personality estimation. To
this end, we propose a multi-modal deep neural network that combines raw audio
and visual information alongside predictions of attribute-specific models to
regress apparent personality. We also analyse spatio-temporal aggregation
schemes and the effect of different time intervals on first impressions. We
base our study on the ChaLearn First Impressions dataset, consisting of
one-person conversational videos. Our model shows state-of-the-art results
regressing apparent personality based on the Big-Five model. Furthermore, given
the interpretability nature of our network design, we provide an incremental
analysis on the impact of each possible source of bias on final network
predictions.
