Facial recognition technology which is common across devices and organisation as a method of authentication needs some serious regulation according to Microsoft. The company's blog post calls on the government to impose limits on the use of the technology to regulate it, Mashable reported.
Facial recognition technology which is common across devices and organisation as a method of authentication needs some serious regulation according to Microsoft. The company's blog post calls on the government to impose limits on the use of the technology to regulate it, Mashable reported.
A lot of activist groups and smaller companies have previously requested for similar regulation to prevent abuse of the tech. However, Microsoft is the first tech giant to do so.
Microsoft has made ‘significant improvements' to its facial recognition technology that had difficulties recognizing darker skin tones, especially when it came to women. According to Microsoft's announcement, it has been able to reduce the error rates for darker-skinned men and women by up to 20 times by expanding the training datasets used for its Face API.
Facial recognition technology's role in society is continuously growing; the most obvious use is in unlocking our devices, but that is only a small part of it. Identifying faces is a useful tool for organizations including authorities searching for criminals, social media sites wanting to tag your friends and even MasterCard which wants you to pay with your face rather than PIN-code. Therefore high accuracy rates are crucial.
However a report published by MIT's Media Lab in February showed that the facial recognition systems from Microsoft, IBM, and China's Megvii misidentified the gender of up to 35 percent of darker-skinned women with the “Microsoft gender classifier perform[ing] the best, with zero errors on classifying all males and lighter females.”
“Artificial intelligence technologies are only as good as the data used to train them,” Microsoft outlined in its new post, noting that “higher error rates on females with darker skin highlights an industrywide challenge.” In other words, it seems as though the data used to train the AI technology didn't sufficiently represent diverse skin tones, nor did it effectively factor in varying hairstyles, jewelry and eyewear.
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