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Perceived personality traits attributed to an individual do not have tocorrespond to their actual personality traits and may be determined in part bythe context in which one encounters a person. These apparent traits determine,to a large extent, how other people will behave towards them. Deep neuralnetworks are increasingly being used to perform automated personalityattribution (e.g., job interviews). It is important that we understand thedriving factors behind the predictions, in humans and in deep neural networks.This paper explicitly studies the effect of the image background on apparentpersonality prediction while addressing two important confounds present inexisting literature; overlapping data splits and including facial informationin the background. Surprisingly, we found no evidence that backgroundinformation improves model predictions for apparent personality traits. Infact, when background is explicitly added to the input, a decrease inperformance was measured across all models.

Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks