A.I. Is Solving the Wrong Problem
People don’t make better decisions when given more data, so why do we assume A.I. will?
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On a warm day in 2008, Silicon Valley’s titans-in-the-making found themselves packed around a bulky, blond-wood conference room table. Although they are big names today, the success of their businesses was hardly assured at the time. Jeff Bezos’s Amazon operated on extremely tight margins and was not profitable. They had just launched the cloud computing side business that would become Amazon’s cash cow, but they didn’t know it yet. Sean Parker had been forced out of Facebook, retreating to a role as managing partner of Peter Thiel’s Founders Fund. He was a few years away from a critical investment in Spotify. To his right was Elon Musk, whose electric car company Tesla was in financial crisis and whose rocket company SpaceX had spent millions of dollars producing a rocket that had failed to launch three times already. Across from them, Larry Page was riding high on an acquisition spree at Google but was being sued by Viacom for $1 billion. Also in the room was one of Google’s former employees, a young software engineer named Evan Williams, who had just co-founded a new company called Twitter.
They committed nine hours of their valuable time crammed around that conference room table to listen to Nobel-Prize-winning scientists teach them about how human beings make decisions. They would apply this knowledge to give their technology companies an edge over the competition using what would eventually be called artificial intelligence.
No time on the agenda was devoted to the issue of data quality. The scientists in the room were behavioral economists, for whom the fantasy of an agent — either human or machine — being able to make completely rational decisions was something to critique and debunk. They didn’t talk about the problems of getting perfect data to build perfect decision-making machines because they didn’t believe that either thing was possible. Instead of eliminating human biases, they wanted to organize technology around those biases.
More and more data
For as long as the Department of Defense (DOD) has collected data, it has spent billions of dollars attempting to “clean” it. The dream of a master…
