Microsoft improves facial recognition error rates across skin tones, gender
Microsoft today announced that information technology has improved its facial recognition API to amend recognize gender and darker peel tones, addressing two weak points in currently available recognition technologies. The improvements, Microsoft says, have led to a reduction in error rates for men and women with darker pare by up to 20 times, and overall error rates for women by nine times.
Currently, facial recognition tech tends to perform best on men with lighter skin and worst on women with darker skin. Microsoft pins this built-in bias on the data used to train the AI behind facial recognition systems. In order to improve its accuracy, the company needed to cistron in a variety of people, as well as other factors like hairstyle, jewelry, and eyewear.
In addressing the trouble, Microsoft'southward team of researchers sought to focus on three key areas of improvement for its datasets: age, gender, and pare tones. Hanna Wallach, a researcher in Microsoft'due south New York Lab said:
We had conversations most dissimilar ways to detect bias and operationalize fairness. Nosotros talked near data collection efforts to diversify the training information. We talked about dissimilar strategies to internally test our systems earlier nosotros deploy them.
Going forward, Microsoft is looking to bring what it has learned nearly the elimination of bias in its AI tools to its other services. The effort will bridge "from idea creation and data collection to model training, deployment and monitoring," Microsoft says. Wallach explains:
If we are grooming car learning systems to mimic decisions made in a biased gild, using information generated by that social club, and then those systems volition necessarily reproduce its biases. This is an opportunity to really call back nigh what values we are reflecting in our systems and whether they are the values we want to be reflecting in our systems.
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Source: https://www.windowscentral.com/microsoft-improves-facial-recognition-error-rates-across-skin-tones-gender
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