SPYING ON YOUR EMOTIONS

 Tech companies now use AI to analyze your feelings in job interviews and public spaces. But the software is prone to racial, cultural and gender bias.

The Zenus setup is one example of a new technology—called

emotion AI or effective computing—that combines cameras and

other devices with artificial-intelligence programs to capture facial expressions, body language, vocal intonation, and other cues.

The goal is to go beyond facial recognition and identification to reveal something previously invisible to technology: the inner feelings, motivations and attitudes of the people in the images. “Cameras have been dumb,” says A.C.L.U. senior policy analyst Jay Stanley, author of the 2019 report  The Dawn of Robot Surveillance.  “Nowthey’re getting smart. They are waking up. They are gaining the ability not just to dumbly record what we do but to make judg-ments about it.”

Emotion AI has become a popular market research tool—at an other trade show, Zenus told Hilton Hotels that a puppies-and-ice cream event the company staged was more engaging than the event’s open bar—but its reach extends into areas where the stakes

are much higher. Systems that read cues of feeling, character and intent are being used or tested to detect threats at border check points, evaluate job candidates, monitor classrooms for boredom or disruption, and recognize signs of aggressive driving. Major automakers are putting the technology into coming generations of vehicles, and Amazon, Micro­soft, Google and other tech companies offer cloud-based emotion-AI services, often bundled with facial recognition. Dozens of start-ups are rolling out applications to help companies make hiring decisions. The practice has become so common in South Korea, for instance, that job coaches often make their clients practice going through AI interviews.AI systems use various kinds of data to generate insights into emotion and behavior. In addition to facial expressions, vocal in tonation, body language and gait, they can analyze the content of spoken or written speech for affect and attitude. Some applications  use the data they collect to probe not for emotions but for related insights, such as what kind of personality a person has and wheth-

er he or she is paying attention or poses a potential threat. But critics warn that emotion AI’s reach exceeds its grasp in potentially hazardous ways. AI algorithms can be trained on data sets with embedded racial, ethnic and gender biases, which in turn can prejudice their evaluations—against, for example, nonwhite job applicants. “There’s this idea that we can off-load some of our cog-

nitive processes on these systems,” says Lauren Rhue, an information systems scientist at the University of Maryland, who has studied racial bias in emotion AI. “That we can say, ‘Oh, this person has

a demeanor that’s threatening’ based on them. That’s where we’regetting into a dangerous area.”

The underlying science is also in dispute. Many emotion-AI apps trace their origins to research conducted half a century ago by psychologists Paul Ekman and Wallace Friesen, who theorized

that a handful of facial expressions correspond to basic emotions (anger, disgust, fear, happiness, sadness and surprise; Ekman later added contempt to the list) and that these expressions form a

universally understood emotional language. But these ideas are now hotly debated. Scientists have found evidence of significant n Liverpool, England, at a February 2020 conference on the rather unglamorous topic of government purchasing, attendees circulated through exhibitor and vendor displays, lingering at some, bypassing others. They were being closely watched. Around the floor,

24 discreetly positioned cameras tracked each person’s movements and cataloged subtle contractions in individuals’ facial muscles at five to 10 frames per second as they reacted to different displays. The images were fed to a computer network, where artificial-intelligence algorithms assessed each person’s gender and age group and analyzed their expressions for signs of “happiness” and “engagement.”INSIDE OUT: S ome emo-cultural and individual variations in facial ex-

based on five basic personality traits, a commontion-AI systems rely on pressions. Many researchers say algorithms can model in psychology shorthanded as OCEAN:

work by psychologist Paul not—yet, anyway—consistently read the subtle-openness to experience, conscientiousness, extra Ekman. He argues universal ties of human expressions in different individu-

version, agreeableness and neuroticism. Recruit-facial expressions reveal als, which may not match up with stereotypical ers receive a ranked list of candidates based on

feelings that include ( from internal feelings. Ekman himself, who worked to

how well each profile fits the job.left ) sadness, happiness,

develop early forms of emotion-recognition tech-Such software is starting to change how busi-

anger, fear and surprise.nology, now argues it poses a serious threat to

ness decisions are made and how organizations privacy and should be heavily regulated.

interact with people. It has reshaped the hiring Emotion AI is not intrinsically bad. If ma-

process at Airtame, instantly elevating some can-chines can be trained to reliably interpret emotions and behavior, didates over others. Gray says that is because the profiling works. the potential for robotics, health care, automobiles, and other fields He shared a chart showing that the job performance of several re-is enormous, experts say. But right now the field is practically a cent hires in sales tracked their personality scores, with employ-free-for-all, and a largely unproven technology could become ubiq- ees who had scored higher in conscientiousness, agreeableness uitous before societies have time to consider the potential costs.and openness doing the best.