Blind Spots in AI Just Might Help Protect Your Privacy

Machine learning, for all its benevolent potential to detect cancers and create collision-proof self-driving cars, also threatens to upend our notions of what's visible and hidden. It can, for instance, enable highly accurate facial recognition, see through the pixelation in photos, and even--as Facebook's Cambridge Analytica scandal showed--use public social media data to predict more sensitive traits like someone's political orientation. Those same machine-learning applications, however, also suffer from a strange sort of blind spot that humans don't--an inherent bug that can make an image classifier mistake a rifle for a helicopter, or make an autonomous vehicle blow through a stop sign. Those misclassifications, known as adversarial examples, have long been seen as a nagging weakness in machine-learning models. Just a few small tweaks to an image or a few additions of decoy data to a database can fool a system into coming to entirely wrong conclusions.