Graduate student Joy Buolamwini tested three different face-recognition systems and found that the accuracy is best when the subject is a lighter skinned man, reports Timothy Revell for New Scientist. With facial recognition software being used by police to identify suspects, “this means inaccuracies could have consequences, such as systematically ingraining biases in police stop and searches,” writes Revell.
Molly Wood at Marketplace speaks with Media Lab graduate student Joy Buolamwini about the findings of her recent research, which examined widespread bias in AI-supported facial recognition programs. “At the end of the day, data reflects our history, and our history has been very biased to date,” Buolamwini said.
Recent research from graduate student Joy Buolamwini shows that facial recognition programs, which are increasingly being used by law enforcement, are failing to identify non-white faces. “When these systems can’t recognize darker faces with as much accuracy as lighter faces, there’s a higher likelihood that innocent people will be targeted by law enforcement,” writes Katharine Schwab for Co. Design.
Writing for Gizmodo, Sidney Fussell explains that a new Media Lab study finds facial-recognition software is most accurate when identifying men with lighter skin and least accurate for women with darker skin. The software analyzed by graduate student Joy Buolamwini “misidentified the gender of dark-skinned females 35 percent of the time,” explains Fussell.
A study co-authored by MIT graduate student Joy Buolamwini finds that facial-recognition software is less accurate when identifying darker skin tones, especially those of women, writes Josh Horwitz of Quartz. According to the study, these errors could cause AI services to “treat individuals differently based on factors such as skin color or gender,” explains Horwitz.
Natasha Singer of The New York Times writes about a joint course from MIT and Harvard to teach the ethics and regulation of artificial intelligence. “As we start to see things, like autonomous vehicles, that clearly have the ability to save people but also cause harm, I think that people are scrambling to build a system of ethics,” says Joi Ito, director of the MIT Media Lab.
Steve Lohr writes for the New York Times about graduate student Joy Buolamwini’s findings on the biases of artificial intelligence in facial recognition. “You can’t have ethical A.I. that’s not inclusive,” Buolamwini said. “And whoever is creating the technology is setting the standards.”
Profs. James DiCarlo and Daniela Rus talk to the BBC’s Click about MIT Intelligence Quest. As they explain, the program aims to encourage discoveries in the areas of natural and artificial intelligence.
In an article for Fortune, writer Larissa Zimberoff highlights Wise Systems, a startup launched out of a graduate entrepreneurship class. With machine learning, Wise creates algorithms for more efficient delivery routes, which “translates into fuel savings, lower wear and tear on trucks, and, for the driver, improved earnings based on higher productivity,” writes Zimberoff.
As Steve Rosenbush notes in the Wall Street Journal, MIT Intelligence Quest hopes to answer two questions: “How does human intelligence work, in engineering terms? And how can we use that deep grasp of human intelligence to build wiser and more useful machines?” explains President Reif.
Dean Anantha Chandrakasan speaks with Kristin Toussaint of Metro about MIT Intelligence Quest. “When you bring together researchers from different disciplines, they end up collaborating and creating something very new that they individually couldn't have created,” said Chandrakasan.
Brian Heater writes for TechCrunch about the new Institute-wide initiative, MIT Intelligence Quest. “Many of the products are moonshoots. They involve teams of scientists and engineers working together. It’s essentially a new model and we need folks and resources behind that,” said Prof. James DiCarlo.
“Intriguing is that [MIT IQ] brings together researchers who study both human intelligence and artificial intelligence. The hope is that better understanding both of them will help develop new algorithms and techniques that can be used broadly,” writes Sean Captain for Fast Company.
MIT is launching a new initiative known as MIT Intelligence Quest, “in an effort to combine multiple disciplines to reverse engineer human intelligence, create new algorithms for machine learning and artificial intelligence and foster collaboration,” writes Larry Dignan for ZDNet.
MIT Intelligence Quest, a new Institute-wide initiative that will advance human and machine intelligence research, “will help connect researchers across disciplines and support projects in which they work together, as well as seek collaborations with industry,” writes Andy Rosen for The Boston Globe.