Gigaom considers the privacy and civil liberties issues that can arise from the increasing data-gathering on individuals’ activities:
The potential benefits of “big data” have been well described, both by us and others: the ability to spot flu trends earlier and potentially save lives, for example, or to make it easier for companies to provide services in a more personalized way. But these same tools could also be used for more disturbing purposes that smack of Orwell’s Big Brother, and two prominent digital skeptics — Nicholas Carr and Evgeny Morozov — recently raised warning flags about that prospect. Which kind of future will we get?
Carr looked at a recent speech from PayPal co-founder Max Levchin at the DLD conference in Germany (one Om also attended, where he conducted an in-depth interview with Levchin) and clearly didn’t like what he saw. Levchin’s view of people, according to Carr, is that they are just resources that are not being utilized efficiently, and the technology of sensors and real-time information can be used to improve that, in much the same way that programmers try to optimize the clock cycles of a microprocessor. […]
Levchin goes on to paint a picture of a future in which his insurance company learns — via sensors in his car — that he is taking his children to work, and boosts his insurance premium by a few dollars for the extra risk (Note: we’ll be talking more about the potential of big data at our Structure:Data conference in New York).
Levchin no doubt sees this as efficient, but Carr sees the looming shadow of Big Brother: What if those same sensors detected that you were overweight, or had eaten too much pizza, he asks — would they report that to your insurance company? […]
In a recent piece for Slate, Carr’s fellow digital skeptic Evgeny Morozov looked at the potential implications of banks and other credit-issuing agencies using big data to determine who deserves a loan. Although he says the idea of big data is “mostly big hype,” Morozov talks about several companies that are trying to use data from all kinds of sources — including social networks such as Facebook and Twitter — to figure out who is credit-worthy.