Facial recognition technology can expose political orientation from facial images even when controlling for demographics and self-presentation

Abstract: A facial recognition algorithm was used to extract face descriptors from carefully standardized images of 591 neutral faces taken in the laboratory setting. Face descriptors were entered into a cross-validated linear regression to predict participants' scores on a political orientation scale (Cronbach's alpha=.94) while controlling for age, gender, and ethnicity. The model's performance exceeded r=.20: much better than that of human raters and on par with how well job interviews predict job success, alcohol drives aggressiveness, or psychological therapy improves mental health. Moreover, the model derived from standardized images performed well (r=.12) in a sample of naturalistic images of 3,401 politicians from the U.S., UK, and Canada, suggesting that the associations between facial appearance and political orientation generalize beyond our sample. The analysis of facial features associated with political orientation revealed that conservatives had larger lower faces, although political orientation was only weakly associated with body mass index (BMI). The predictability of political orientation from standardized images has critical implications for privacy, regulation of facial recognition technology, as well as the understanding the origins and consequences of political orientation.