In a blog post today, Microsoft announced an update to Face API that improves the facial recognition platform’s ability to recognize gender across different skin tones, a longstanding challenge for computer vision platforms.
With the improvements, the Redmond company said, it was able to reduce error rates for men and women with darker skin by up to 20 times, and by 9 times for women.
For years, researchers have demonstrated facial ID systems’ susceptibility to ethnic bias. A 2011 study found that algorithms in China, Japan, and South Korea had more trouble distinguishing between Caucasian faces than faces of East Asians, and a separate study showed that widely deployed facial recognition tech from security vendors performed 5 to 10 percent worse on African American faces.
To tackle the problem, researchers at Microsoft revised and expanded Face API’s training and benchmark datasets and collected new data across skin tones, genders, and ages. It also worked with experts in artificial intelligence (AI) fairness to improve the precision of the algorithm’s gender classifier.
“We had conversations about different ways to detect bias and operationalize fairness,” Hanna Wallach, senior researcher at Microsoft’s New York research lab, said in a statement. “We talked about data collection efforts to diversify the training data. We talked about different strategies to internally test our systems before we deploy them.”
The enhanced Face AI tech is just the start of a company-wide effort to minimize bias in AI. Microsoft is developing tools that help engineers identify blind spots in…