Stanford researchers have developed a new and principled approach to calculating the value of data used to train AI models based on a Nobel Prize-winning economics method that improves determining the value of individual data points or datasets. It can also help AI systems designers identify low value data that should be excluded as well as high value data worth acquiring and even used to reduce bias in AI systems.