Cliff Kuang
The New York Times Magazine
Originally published November 21, 2017
Here are two excerpts:
In 2018, the European Union will begin enforcing a law requiring that any decision made by a machine be readily explainable, on penalty of fines that could cost companies like Google and Facebook billions of dollars. The law was written to be powerful and broad and fails to define what constitutes a satisfying explanation or how exactly those explanations are to be reached. It represents a rare case in which a law has managed to leap into a future that academics and tech companies are just beginning to devote concentrated effort to understanding. As researchers at Oxford dryly noted, the law “could require a complete overhaul of standard and widely used algorithmic techniques” — techniques already permeating our everyday lives.
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“Artificial intelligence” is a misnomer, an airy and evocative term that can be shaded with whatever notions we might have about what “intelligence” is in the first place. Researchers today prefer the term “machine learning,” which better describes what makes such algorithms powerful. Let’s say that a computer program is deciding whether to give you a loan. It might start by comparing the loan amount with your income; then it might look at your credit history, marital status or age; then it might consider any number of other data points. After exhausting this “decision tree” of possible variables, the computer will spit out a decision. If the program were built with only a few examples to reason from, it probably wouldn’t be very accurate. But given millions of cases to consider, along with their various outcomes, a machine-learning algorithm could tweak itself — figuring out when to, say, give more weight to age and less to income — until it is able to handle a range of novel situations and reliably predict how likely each loan is to default.
The article is here.
