Big Data's Dehumanizing Impact On Public Policy

Matt Asay:

One of the mantras of the Big Data revolution is that causation no longer matters. It's enough, the theory goes, to seek correlations in our copious data, deciphering "what" is happening and not bothering with "why." But not only is this problematic for a business looking for optimal retail pricing strategies, it's dramatically more so for those charged with crafting public policy.

For governments and other public institutions, it turns out that understanding causation matters a great deal.

Causation Loses Its Sex Appeal

The "forget-causation-seek-correlation" Big Data crowd has been around for years and its most sophisticated proponents are Kenneth Cukier (The Economist) and Viktor Mayer-Schönberger (Oxford University). In their excellent Big Data, the authors argue: "In a big-data world ... we won't have to be fixated on causality; instead we can discover patterns and correlations in the data that offer us novel and invaluable insights. Big data is about what, not why."

The idea is that given enough data, algorithms can appreciate correlations between seemingly disparate data sets without bothering to understand those correlations. It is enough to see that a rise in the purchase of Pop-Tarts at Wal-Mart highly correlates with hurricane warnings. Wal-Mart needn't understand why: it just needs to stock Pop-Tarts in a visible area of the store whenever hurricane warnings are issued.

Posted by Jim Zellmer at July 16, 2013 12:33 AM
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