Don’t trust AI until we build systems that earn trust — from
Progress in artificial intelligence belies a lack of transparency that is vital for its adoption, says Gary Marcus, coauthor of “Rebooting AI”


Mr Marcus argues that it would be foolish of society to put too much stock in today’s AI techniques since they are so prone to failures and lack the transparency that researchers need to understand how algorithms reached their conclusions.

As part of The Economist’s Open Future initiative, we asked Mr Marcus about why AI can’t do more, how to regulate it and what teenagers should study to remain relevant in the workplace of the future.

Trustworthy AI has to start with good engineering practices, mandated by laws and industry standards, both of which are currently largely absent. Too much of AI thus far has consisted of short-term solutions, code that gets a system to work immediately, without a critical layer of engineering guarantees that are often taken for granted in other field. The kinds of stress tests that are standard in the development of an automobile (such as crash tests and climate challenges), for example, are rarely seen in AI. AI could learn a lot from how other engineers do business.

The assumption in AI has generally been that if it works often enough to be useful, then that’s good enough, but that casual attitude is not appropriate when the stakes are high. It’s fine if autotagging people in photos turns out to be only 90 percent reliable—if it is just about personal photos that people are posting to Instagram—but it better be much more reliable when the police start using it to find suspects in surveillance photos.