Three Paradoxes of Big Data

Two legal experts call out the dangers of sophisticated data analytics, left unchecked.

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The 1997 sci-fi film Gattaca recently came back into headlines when a Wikipedia summary of the movie’s plot was apparently used as the basis of a speech by Kentucky Senator Rand Paul. The movie presents a society where DNA determines social class. A database identifies genetically superior individuals — termed “valids” — while winnowing out their naturally conceived “in-valid” counterparts.

The valids have predetermined life — and career — paths, unalterable by desire, capability, circumstance or happenstance. The in-valids have an equally predetermined destiny, one that leaves them on a much lower social scale.

Senator Paul’s agenda notwithstanding, the question is worth asking: Could our society ever use genetics, biometrics and, essentially, predictive analytics to determine an individual’s path, as in Gattaca?

That risk may become real, according to a recent Stanford Law Review paper, Three Paradoxes of Big Data, if big data continues on its current course. “We want to suggest that the utopian rhetoric of big data is frequently overblown, and that a less wild-eyed and more pragmatic discussion of big data would be more helpful,” write Neil Richards, professor of law at Washington University, and Jonathan King, who is pursuing an advanced legal degree at Washington University School of Law and serves as the vice president of cloud strategy and business development at Savvis.

The two suggest that while there are clearly benefits to be derived from mining large data sets using sophisticated analytics — from the potential to conserve precious resources to tracking and curing lethal diseases — there are also implications (inherent dangers, really) of which the public needs to be aware.

The authors frame three paradoxes around transparency, identity and power that are the result of the big data movement — and suggest ways to move forward.

The Transparency Paradox:

Big data is really the amalgamation of little data inputs — information about people, places and things collected by sensors, cell phones, click patterns and other data-generating mechanisms. These data inputs are collected by commercial and government systems, for big data purposes.

Topics

Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
More in this series

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