Artificial intelligence—specifically, machine learning—is a part of daily life for computer and smartphone users. From autocorrecting typos to recommending new music, machine learning algorithms can help make life easier. They can also make mistakes.
It can be challenging for computer scientists to figure out what went wrong in such cases. This is because many machine learning algorithms learn from information and make their predictions inside a virtual “black box,” leaving few clues for researchers to follow.
A group of computer scientists at the University of Maryland has developed a promising new approach for interpreting machine learning algorithms.