Principal Investigator Luca Daniel
As AI systems automate more tasks, the need to quantify their vulnerability and alert the public to possible failures has taken on new urgency, especially in safety-critical applications like self-driving cars and fairness-critical applications like hiring and lending. To address the problem, MIT-IBM researchers are developing a method that reports how much each individual input can be altered before the neural network makes a mistake, on their own or through a malicious attack. The team is now expanding the framework to larger, and more general neural networks, and developing tools to quantify their level of vulnerability based on many different ways of measuring input-alteration.