Trust But Verify: Peeking Inside the "Black Box" of Machine Learning

Artificial intelligence can be a powerful tool for analyzing massive amounts of data, finding connections and correlations that humans can't. However, unlike a person solving a math problem, many AI models can't easily explain the steps they took to reach their final answers. They are what's known in computer science as black boxes: You can see what goes in and what comes out; what happens in between is a mystery. The black-box problem is baked into many machine learning models, explains Laura Blattner, an assistant professor of finance at Stanford GSB. "The power of the technology is its ability to reflect the complexity in the world," she says.