What is your data worth? Quantifying the value of data in machine learning.
As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, CA Gov. Newsom recently proposed "data dividend" whereby consumers are compensated by companies for the data that they generate. In this talk we will present a principled framework to quantify the value of different data in machine learning. Beyond regulatory implications, we will discuss applications to denoising, active learning, and domain adaption on large biomedical datasets. I will conclude by discussing how data valuation contributes to a broader framework of accountable AI.
James Zou is an assistant professor of biomedical data science, CS and EE at Stanford University. He is also an inaugural Chan-Zuckerberg Investigator and is the faculty director of the Stanford AI for Health program. His group develops novel machine learning and deep learning algorithms that have strong statistical guarantees; and several of his methods are currently deployed by biotech and tech companies. His research have been published in Nature, Nature Biotech, PNAS, Nature Methods, and have been recognized with several best paper awards as well as the Google and Tencent Faculty Awards.