论文标题
衡量可信度或自动化的phosighosy？对Safra，Chevallier，Grèzes和Baumard的评论（2020）
Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Grèzes, and Baumard (2020)
论文作者
论文摘要
人际信任 - 共同表现出对其他人的信心和脆弱性 - 可以看作是人类社会发展的工具。 Safra，Chevallier，Grèzes和Baumard（2020）通过训练机器学习（ML）算法研究了人际信任的历史发展，以根据面部特征产生基于历史肖像的可信度评级。他们报告说，肖像的可信度评级随着时间的推移增加了1500--2000ce之间，声称这证明了人际信任的更大增长与社会进步的几个指标相吻合。我们认为，这些主张被几个方法论和分析问题混淆，并突出了Safra等人的算法与Phandiephosy伪科学之间的混乱相似之处。我们将进一步详细讨论这些问题的含义和潜在的现实后果。
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, Grèzes, 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.
