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DannyB [soylentnews.org] writes:

Facial recognition technology can expose political orientation from naturalistic facial images [nature.com]

Abstract
Ubiquitous facial recognition technology can expose individuals’ political orientation, as faces of liberals and conservatives consistently differ. A facial recognition algorithm was applied to naturalistic images of 1,085,795 individuals to predict their political orientation by comparing their similarity to faces of liberal and conservative others. Political orientation was correctly classified in 72% of liberal–conservative face pairs, remarkably better than chance (50%), human accuracy (55%), or one afforded by a 100-item personality questionnaire (66%). Accuracy was similar across countries [ . . . ]

Introduction
There is a growing concern that the widespread use of facial recognition will lead to the dramatic decline of privacy and civil liberties^{[1] [nature.com]} [ . . . ]

Pervasive surveillance is not the only risk brought about by facial recognition. Apart from identifying individuals, the algorithms can identify individuals’ personal attributes, as some of them are linked with facial appearance. Like humans, facial recognition algorithms can accurately infer gender, age, ethnicity, or emotional state.^{[2] [nature.com]},^{[3] [nature.com]} Unfortunately, the list of personal attributes that can be inferred from the face extends well beyond those few obvious examples.

A growing number of studies claim to demonstrate that people can make face-based judgments of honesty^{[4] [nature.com]}, personality^{[5] [nature.com]}, intelligence^{[6] [nature.com]}, sexual orientation^{[7] [nature.com]}, political orientation^{[8] [nature.com]},^{[9] [nature.com]},^{[10] [nature.com]},^{[11] [nature.com]},^{[12] [nature.com]}, and violent tendencies^{[13] [nature.com]}.

[...] the accuracy of the human judgment is relatively low. For example, when asked to distinguish between two faces—one conservative and one liberal—people are correct about 55% of the time (derived from Cohen’s d reported in Tskhay and Rule^{[15] [nature.com]}), only slightly above chance (50%). Yet, as humans may be missing or misinterpreting some of the cues, their low accuracy does not necessarily represent the limit of what algorithms could achieve. Algorithms excel at recognizing patterns in huge datasets that no human could ever process^{[16] [nature.com]}, and are increasingly outperforming us in visual tasks ranging from diagnosing skin cancer^{[17] [nature.com]} to facial recognition^{[18] [nature.com]} to face-based judgments of intimate attributes, such as sexual orientation (76% vs. 56%)7,^{[19] [nature.com]}, personality (64% vs. 57%; derived from Pearson’s rs)^{[20] [nature.com]},^{[21] [nature.com]},^{[22] [nature.com]}, and—as shown here—political orientation.

Methods
We used a sample of 1,085,795 participants from three countries (the U.S., the UK, and Canada; see Table 1 [nature.com]) and their self-reported political orientation, age, and gender. Their facial images (one per person) were obtained from their profiles on Facebook or a popular dating website.

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Original Submission