The Hidden Biases in Big Data

Everybody is in love with big data, dreaming that if only the data points were all connected some sort of deep truth will be revealed. That's never going to be the case, says Kate Crawford.

Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations. Hidden biases in both the collection and analysis stages present considerable risks, and are as important to the big-data equation as the numbers themselves.


She cites a couple of examples to support her point: Big data uncovered some interesting social phenomenon during Hurricane Sandy. The day after it passed Florida, a bunch of people there hit the nightclubs. But in New York City, the high ground of Manhattan was abuzz in Twitter, but Brooklyn, waist deep in floodwater, was largely silent. Similarly, a pothole reporting app in Boston makes it seem as if the poorest neighborhoods have the fewest potholes. In fact, potholes make no socioeconomic distinctions.

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