# Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Gr`ezes, and Baumard (2020)

arXiv: https://arxiv.org/abs/2202.08674 · HTML: https://mlsanity.com/paper/2202.08674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 17 Feb 2022.

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

- [Towards decolonising computational sciences](https://mlsanity.com/paper/2009.14258/) (2020)
- [Algorithmic Injustices: Towards a Relational Ethics](https://mlsanity.com/paper/1912.07376/) (2019)
- [A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images](https://mlsanity.com/paper/1902.10739/) (2019)
- [Automated Inference on Sociopsychological Impressions of Attractive Female Faces](https://mlsanity.com/paper/1612.04158/) (2016)

## Built on by (most cited first, 1 of 1)

- [From Modelling to Understanding Children's Behaviour in the Context of Robotics and Social Artificial Intelligence](https://mlsanity.com/paper/2210.11161/) (2022)

## Abstract (arXiv)

Interpersonal trust - a shared display of confidence and vulnerability toward other individuals - can be seen as instrumental in the development of human societies. Safra, Chevallier, Gr`ezes, and Baumard (2020) studied the historical progression of interpersonal trust by training a machine learning (ML) algorithm to generate trustworthiness ratings of historical portraits, based on facial features. They reported that trustworthiness ratings of portraits dated between 1500--2000CE increased with time, claiming that this evidenced a broader increase in interpersonal trust coinciding with several metrics of societal progress. We argue that these claims are confounded by several methodological and analytical issues and highlight troubling parallels between Safra et al.'s algorithm and the pseudoscience of physiognomy. We discuss the implications and potential real-world consequences of these issues in further detail.