Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks
This addresses a key factor in automated personality attribution for applications like job interviews, showing that background is not a useful feature, which is an incremental finding.
The study investigated whether image background influences apparent personality predictions in deep neural networks, finding that background information does not improve predictions and actually decreases performance across all models.
Perceived personality traits attributed to an individual do not have to correspond to their actual personality traits and may be determined in part by the context in which one encounters a person. These apparent traits determine, to a large extent, how other people will behave towards them. Deep neural networks are increasingly being used to perform automated personality attribution (e.g., job interviews). It is important that we understand the driving factors behind the predictions, in humans and in deep neural networks. This paper explicitly studies the effect of the image background on apparent personality prediction while addressing two important confounds present in existing literature; overlapping data splits and including facial information in the background. Surprisingly, we found no evidence that background information improves model predictions for apparent personality traits. In fact, when background is explicitly added to the input, a decrease in performance was measured across all models.