A neural network learns when it should not be trusted

Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis. These networks are good at recognizing patterns in large, complex datasets to aid in decision-making. But how do we know they're correct? Alexander Amini and his colleagues at MIT and Harvard University wanted to find out. They've developed a quick way for a neural network to crunch data, and output not just a prediction but also the model's confidence level based on the quality of the available data.