# Facial recognition technology and human raters can predict political orientation from images of expressionless faces even when controlling for demographics and self-presentation

arXiv: https://arxiv.org/abs/2303.16343 · HTML: https://mlsanity.com/paper/2303.16343/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [What your Facebook Profile Picture Reveals about your Personality](https://mlsanity.com/paper/1708.01292/) (2017)
- [A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images](https://mlsanity.com/paper/1902.10739/) (2019)

## Built on by (most cited first, 2 of 2)

- [AI, Entrepreneurs, and Privacy: Deep Learning Outperforms Humans in Detecting Entrepreneurs from Image Data](https://mlsanity.com/paper/2409.03765/) (2024)
- [The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?](https://mlsanity.com/paper/2411.18656/) (2024)

## Abstract (arXiv)

Carefully standardized facial images of 591 participants were taken in the laboratory, while controlling for self-presentation, facial expression, head orientation, and image properties. They were presented to human raters and a facial recognition algorithm: both humans (r=.21) and the algorithm (r=.22) could predict participants' scores on a political orientation scale (Cronbach's alpha=.94) decorrelated with age, gender, and ethnicity. These effects are on par with how well job interviews predict job success, or alcohol drives aggressiveness. Algorithm's predictive accuracy was even higher (r=.31) when it leveraged information on participants' age, gender, and ethnicity. Moreover, the associations between facial appearance and political orientation seem to generalize beyond our sample: The predictive model derived from standardized images (while controlling for age, gender, and ethnicity) could predict political orientation (r=.13) from naturalistic images of 3,401 politicians from the U.S., UK, and Canada. The analysis of facial features associated with political orientation revealed that conservatives tended to have larger lower faces. The predictability of political orientation from standardized images has critical implications for privacy, the regulation of facial recognition technology, and understanding the origins and consequences of political orientation.