  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.
