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Facial recognition technology has progressed to point where it now interprets emotions in facial expressions. This type of analysis is increasingly used in daily life. For example, companies can use facial recognition software to help with hiring decisions. Other programs scan the faces in crowds to identify threats to public safety.
Unfortunately, this technology struggles to interpret the emotions of black faces. My new study, published last month, shows that emotional analysis technology assigns more negative emotions to black men’s faces than white men’s faces.
This isn’t the first time that facial recognition programs have been shown to be biased. Google labeled black faces as gorillas. Cameras identified Asian faces as blinking. Facial recognition programs struggled to correctly identify gender for people with darker skin.
My work contributes to a growing call to better understand the hidden bias in artificial intelligence software.
To examine the bias in the facial recognition systems that analyze people’s emotions, I used a data set of 400 NBA player photos from the 2016 to 2017 season, because players are similar in their clothing, athleticism, age and gender. Also, since these are professional portraits, the players look at the camera in the picture.
I ran the images through two well-known types of emotional recognition software. Both assigned black players more negative emotional scores on average, no matter how much they smiled.
For example, consider the official NBA pictures of Darren Collison and Gordon Hayward. Both players are smiling, and, according to the facial recognition and analysis program Face++, Darren Collison and Gordon Hayward have similar smile scores—48.7 and 48.1 out of 100, respectively.
Basketball players Darren Collision (left) and Gordon Hayward (right). basketball-reference.com
However, Face++ rates Hayward’s expression as 59.7 percent happy and 0.13 percent angry and Collison’s expression as 39.2 percent happy and 27 percent angry. Collison is viewed as nearly as angry as he is happy and far angrier than Hayward—despite the facial recognition program itself recognizing that both players are smiling.
In contrast, Microsoft’s Face API viewed both men as happy. Still, Collison is viewed as less happy than Hayward, with 98 and 93 percent happiness scores, respectively. Despite his smile, Collison is even scored with a small amount of contempt, whereas Hayward has none.
Across all the NBA pictures, the same pattern emerges. On average, Face++ rates black faces as twice as angry as white faces. Face API scores black faces as three times more contemptuous than white faces. After matching players based on their smiles, both facial analysis programs are still more likely to assign the negative emotions of anger or contempt to black faces.
Stereotyped by AI
My study shows that facial recognition programs exhibit two distinct types of bias.
First, black faces were consistently scored as angrier than white faces for every smile. Face++ showed this type of bias. Second, black faces were always scored as angrier if there was any ambiguity about their facial expression. Face API displayed this type of disparity. Even if black faces are partially smiling, my analysis showed that the systems assumed more negative emotions as compared to their white counterparts with similar expressions. The average emotional scores were much closer across races, but there were still noticeable differences for black and white faces.
This observation aligns with other research, which suggests that black professionals must amplify positive emotions to receive parity in their workplace performance evaluations. Studies show that people perceive black men as more physically threatening than white men, even when they are the same size.
Some researchers argue that facial recognition technology is more objective than humans. But my study suggests that facial recognition reflects the same biases that people have. Black men’s facial expressions are scored with emotions associated with threatening behaviors more often than white men, even when they are smiling. There is good reason to believe that the use of facial recognition could formalize preexisting stereotypes into algorithms, automatically embedding them into everyday life.
Until facial recognition assesses black and white faces similarly, black people may need to exaggerate their positive facial expressions—essentially smile more—to reduce ambiguity and potentially negative interpretations by the technology.
Although innovative, artificial intelligence can perpetrate and exacerbate existing power dynamics, leading to disparate impact across racial/ethnic groups. Some societal accountability is necessary to ensure fairness to all groups because facial recognition, like most artificial intelligence, is often invisible to the people most affected by its decisions.
Lauren Rhue, Assistant Professor of Information Systems and Analytics, Wake Forest University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Servosila, a robotics technology company, announced a launch of a new line of robotic arm manipulators specifically targeted at mobile robotics market.
“Servosila Robotic Arms are the first manipulators specifically designed for mobile robots,” – said the company’s spokesperson, – “it is very easy to retrofit any existing robotic chassis or a torso with a Servosila Robotic Arm”.
Servosila Robotic Arms are attachable payload modules for mobile service robots or other robotic platforms. Servosila Robotic Arms shall typically be mounted on a chassis or a torso of a mobile robot and be powered by an on-board power supply system of the host robotic platform.
The robotic arms can be used both outdoors and indoors. The arms are water-tight, dust-proof and function properly in the rain and in the snow. The arms are designed to withstand impacts, collisions with obstacles and, in general, the harsh treatment so common to mobile robotics applications.
The servo drives and external electrical connectors of the robotic arms are water-tight and dust-proof (IP68 rating). The entire arm can be occasionally submersed in water without any adverse effects on its performance. The robotic arms may be operated in cold or hot weather.
Mobile robots tend to bump into things and hit obstacles while on the move. The harsh nature of outdoor mobile robotics applications caused a profound effect on the design of Servosila Robotic Arms, especially on the internal structure of servo drives and their harmonic reduction gears.
There are no exposed cables on the outside of the robotic arms that could be torn off when a mobile robot moves through bushes or forests.
Numerous protection measures built into electronic servo controllers and mechanical parts of Servosila Robotic Arms ensure reliable operation on-board of outdoor mobile service robots.
Servosila Robotic Arms are lightweight by design. For a given lifting capability, Servosila Robotic Arms have a significantly lower weight than their industrial counterparts. The lower weight of a Servosila Robotic Arm enables a mobile robot equipped with the arm to operate longer on a single battery charge, keep its center of gravity lower for better balance, climb stairs easier or have a superior mobility.
When not in an active use, Servosila Robotic Arms can folded into a very compact form that doesn’t occupy much space on the top of a robotic chassis or on the side of a torso. This feature protects the robotic arm of a mobile robot in case of an unexpected collision with an obstacle or whenever a rough terrain is encountered by the mobile robotic platform. The compact folded form also comes handy during transportation.
By folding its robotic arm into the compact form, the robot frees up its working area for other payloads to operate in. This is useful in case the robot is equipped with additional payloads other than the robotic arm.
Photo Credits: Servosila Limited (Hong Kong)
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