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MIT Researchers: Amazon’s Rekognition Shows Gender and Ethnic Bias

Amazon’s facial analysis software distinguishes gender among certain ethnicities less accurately than do competing services from IBM and Microsoft. That’s the conclusion drawn by Massachusetts Institute of Technology researchers in a new study published today, which found that Rekognition, Amazon Web Services’ (AWS) object detection API, fails to reliably determine the sex of female and darker-skinned faces in specific scenarios.

The study’s coauthors claim that in experiments conducted over the course of 2018, Rekognition’s facial analysis feature mistakenly identified pictures of woman as men and darker-skinned women as men 19 percent and 31 percent of the time, respectively. By comparison, Microsoft’s offering misclassified darker-skinned women as men 1.5 percent of the time.

Amazon disputes those findings. It says that internally, in tests of an updated version of Rekognition, it observed “no difference” in gender classification accuracy across all ethnicities. And it notes that the paper in question fails to make clear the confidence threshold — i.e., the minimum precision that Rekognition’s predictions must achieve in order to be considered “correct” — used in the experiments.

In a statement provided to VentureBeat, Dr. Matt Wood, general manager of deep learning and AI at AWS, drew a distinction between facial analysis — which is concerned with spotting faces in videos or images and assigning generic attributes to them — and facial recognition, which matches an individual face to faces in videos and images. He said that it’s “not possible” to conclude the accuracy of facial recognition based on results obtained using facial analysis and argued that the paper “[doesn’t] represent how a customer would use” Rekognition.

“Using an up-to-date version of Amazon Rekognition with similar data downloaded from parliamentary websites and the Megaface dataset of [1 million] images, we found exactly zero false positive matches with the recommended 99 [percent] confidence threshold,” Wood said. “We continue to seek input and feedback to constantly improve this technology, and support the creation of third-party evaluations, datasets, and benchmarks.”

It’s the second time Amazon’s been in hot water over Rekognition’s alleged susceptibility to bias.

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