Researchers at the Georgia Institute of Technology have proposed “the concept of predictive inequity in detecting pedestrians of different skin tones in object detection systems”. In other words, you're more at risk of being hit and killed by a self-driving car if you're not white.
to “reiterate our demand that Amazon stops providing its face surveillance product to the [US] government… [because] Amazon is gravely threatening the safety of community members, ignoring the protests of its own workers, and undermining public trust in its business.”
Similarly, the latest facial or human detection algorithms use machine learning rather than traditional computer vision to improve accuracy. Just as with facial matching, the data on which the AI is trained will result in potential bias in how it performs in the real world. And if the detection engine doesn't see a person, then it's as if they are not there.
The researchers marked-up a dataset of several-thousand pedestrians, separating Fitzpatrick skin tones 1-3 ‘LS’ [light-skinned] from 4-6 ‘DS’ [dark-skinned], and then tested different models to confirm that they did not have an anomaly. They found “that across all models and base architectures studied, performance on light-skin exceeds that of dark-skin, demonstrating that this phenomenon is not specific to a particular model.
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