The Rise of Emotional Surveillance

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If computers are flawed analysts of straightforward productivity, imagine, now, applying that same technology to something as complex as the constellation of emotions expressible by humans. Study after study shows that AI replicates the biases of the data it’s trained on. (In 2018, Lauren Rhue, then a professor of information systems and analytics at Wake Forest University, studied photographs of NBA players and emotion-recognition AI; she discovered that the tech found Black players to be angrier than their white teammates—even, in some cases, if they were smiling.) Many emotion-AI products base their rubrics on the clinical psychologist Paul Ekman’s theory of basic emotions, which holds that all people experience the same six core emotions: anger, disgust, fear, happiness, sadness, and surprise. That theory has been widely challenged as oversimplistic and methodologically flawed in the many decades since it was first published.

Body language is a metaphor that has become a cliché, but anyone who has spent much time at all around other people understands that everyone speaks in a different dialect. “Your movements,” the neuroscientist and psychologist Lisa Feldman Barrett told me, “whether it’s on your face or in your body or the tones that you emit, don’t have inherent emotional meaning. They have relational meaning.” They vary based on the context of the conversation, the physiognomy of the person making them, culture, room temperature, vibes.

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