Artificial Intelligence Has Some Explaining To Do

Software makers offer more transparent machine-learning tools—but there’s a trade-off.

Jeremy Kahn

Artificial intelligence software can recognize faces, translate between Mandarin and Swahili, and beat the world’s best human players at such games as Go, chess, and poker. What it can’t always do is explain itself.

AI is software that can learn from data or experiences to make predictions. A computer programmer specifies the data from which the software should learn and writes a set of instructions, known as an algorithm, about how the software should do that—but doesn’t dictate exactly what it should learn. This is what gives AI much of its power: It can discover connections in the data that would be more complicated or nuanced than a human would find. But this complexity also means that the reason the software reaches any particular conclusion is often largely opaque, even to its own creators.

For software makers hoping to sell AI systems, this lack of clarity can be bad for business. It’s hard for humans to trust a system they can’t understand—and without trust, organizations won’t pony up big bucks for AI software. This is especially true in fields such as health care, finance, and law enforcement, where the consequences of a bad recommendation are more substantial than, say, that time Netflix thought you might enjoy watching The Hangover Part III.

Regulation is also driving companies to ask for more explainable AI. In the U.S., insurance laws require that companies be able to explain why they denied someone coverage or charged them a higher premium than they did their neighbor. In Europe, the General Data Protection Regulation, which took effect in May, gives EU citizens a “right to a human review” of any algorithmic decision affecting them. If the bank rejects your loan application, it can’t just tell you the computer said no—a bank employee has to be able to review the process the machine used or conduct a separate analysis.