The mystification of algorithms

Whenever I read stories on big data, it strikes me that journalists hardly ever know or care to explain what algorithms are or what they do. Take this example from the Economist’s recent special report on big data and politics:

Campaigners are hoovering up more and more digital information about every voting-age citizen and stashing it away in enormous databases. With the aid of complex algorithms, these data allow campaigners to decide, say, who needs to be reminded to make the trip to the polling station and who may be persuaded to vote for a particular candidate.

The Economist, March 26th, Special Report p.4

First, few seemed bothered with making a distinction between inferred intelligence and collected data. The quote above is an example of inferring information from existing databases – trying to figure out what kind of behaviour correlates with voting for a specific party. Since most databases are of a commercial nature,  I am guessing that they are trying to figure out if certain consumer behaviour, like buying organic milk, correlates with voting democrat.

In the case of protest movements, the waves of collective action leave a big digital footprint. Using ever more sophisticated algorithms, governments can mine these data.

The Economist, March 26th, Special Report p.4

The second example is about mining social media for data on dissidents and revolutionary action. There the data itself can be a source of “actionable intelligence” as Oscar Gandy would put it. There is nothing inherently sophisticated in looking for evidence of people participating in protest events on Facebook or finding protest movement chatter on Twitter.

Second, while the algorithms might be complex, they are usually employed in programmes that have relatively clear user interfaces. The Citizen Lab at the University of Toronto demonstrated that “net nannying” tools that are employed in schools, homes or businesses are also frequently used by authoritarian states for monitoring a whole nation’s communications.

While these reports give some insight into how data science is used to gain an advantage in politics or law enforcement, they tend to mystify the technologies and techniques involved. We are left confounded by this data magic that somehow changes the playing field. But the guiding principles are not that hard to understand, and using the programmes do not require a degree in computer science. We might not know exactly how the algorithms work, but we know what sources of information they use and what their purposes are.

Prism-slide-9
Slide illustrating how to search PRISM’s counterterrorism database

 

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