The Filter Bubble - Eli Pariser [53]
It’s this humility that many algorithmic prediction methods fail to build in. Sure, they encounter people or behaviors that don’t fit the mold from time to time, but these aberrations don’t fundamentally compromise their algorithms. After all, the advertisers whose money drives these systems don’t need the models to be perfect. They’re most interested in hitting demographics, not complex human beings.
When you model the weather and predict there’s a 70 percent chance of rain, it doesn’t affect the rain clouds. It either rains or it doesn’t. But when you predict that because my friends are untrustworthy, there’s a 70 percent chance that I’ll default on my loan, there are consequences if you get me wrong. You’re discriminating.
The best way to avoid overfitting, as Popper suggests, is to try to prove the model wrong and to build algorithms that give the benefit of the doubt. If Netflix shows me a romantic comedy and I like it, it’ll show me another one and begin to think of me as a romantic-comedy lover. But if it wants to get a good picture of who I really am, it should be constantly testing the hypothesis by showing me Blade Runner in an attempt to prove it wrong. Otherwise, I end up caught in a local maximum populated by Hugh Grant and Julia Roberts.
The statistical models that make up the filter bubble write off the outliers. But in human life it’s the outliers who make things interesting and give us inspiration. And it’s the outliers who are the first signs of change.
One of the best critiques of algorithmic prediction comes, remarkably, from the late-nineteenth-century Russian novelist Fyodor Dostoyevsky, whose Notes from Underground was a passionate critique of the utopian scientific rationalism of the day. Dostoyevsky looked at the regimented, ordered human life that science promised and predicted a banal future. “All human actions,” the novel’s unnamed narrator grumbles, “will then, of course, be tabulated according to these laws, mathematically, like tables of logarithms up to 108,000, and entered in an index ... in which everything will be so clearly calculated and explained that there will be no more incidents or adventures in the world.”
The world often follows predictable rules and falls into predictable patterns: Tides rise and fall, eclipses approach and pass; even the weather is more and more predictable. But when this way of thinking is applied to human behavior, it can be dangerous, for the simple reason that our best moments are often the most unpredictable ones. An entirely predictable life isn’t worth living. But algorithmic induction can lead to a kind of information determinism, in which our past clickstreams entirely decide our future. If we don’t erase our Web histories, in other words, we may be doomed to repeat them.
5
The Public Is Irrelevant
The presence of others who see what we see and hear what we hear assures us of the reality of the world and ourselves.
—Hannah Arendt
It is an axiom of political science in the United
States that the only way to neutralize the influence
of the newspapers is to multiply their number.
—Alexis de Tocqueville
On the night of May 7, 1999, a B-2 stealth bomber left Whiteman Air Force Base in Missouri. The aircraft flew on an easterly course until it reached the city of Belgrade in Serbia, where a civil war was under way. Around midnight local time, the bomber delivered its cargo: four GPSGUIDED bombs, into which had been programmed an address that CIA documents identified as a possible arms warehouse. In fact, the address was the Yugoslavian Chinese Embassy. The building was demolished, and three Chinese diplomats were killed.
The United States immediately apologized, calling the event an accident. On Chinese state TV,