V zadnjem desetletju niso samo internetni brskalniki (Google, Yahoo, etc.) ali borzno poslovanje postali domena zmogljivih algoritmov, ki tečejo na superračunalnikih, pač pa tudi spremljanje komunikacij s strani policije, uradnih in zasebnih tajnih služb. Ta članek v Guardianu, ki ekskluzivno objavlja NSA leakse iz Snowdenovega arhiva, pojasnjuje, kako policija uporablja algoritme za iskanje “signalov” v “hrupu” velike količine komunikacijskih podatkov (big data).
The use of algorithms in policing is one example of their increasing influence on our lives. And, as their ubiquity spreads, so too does the debate around whether we should allow ourselves to become so reliant on them – and who, if anyone, is policing their use. Such concerns were sharpened further by the continuing revelations about how the US National Security Agency (NSA) has been using algorithms to help it interpret the colossal amounts of data it has collected from its covert dragnet of international telecommunications.
“For datasets the size of those the NSA collect, using algorithms is the only way to operate for certain tasks,” says James Ball, the Guardian’s data editor and part of the paper’s NSA Files reporting team. “The problem is how the rules are set: it’s impossible to do this perfectly. If you’re, say, looking for terrorists, you’re looking for something very rare. Set your rules too tight and you’ll miss lots of, probably most, potential terror suspects. But set them more broadly and you’ll drag lots of entirely blameless people into your dragnet, who will then face further intrusion or even formal investigation. We don’t know exactly how the NSA or GCHQ use algorithms – or how extensively they’re applied. But we do know they use them, including on the huge data trawls revealed in the Guardian.”
Parpas stresses that algorithms are not a new phenomenon: “They’ve been used for decades – back to Alan Turing and the codebreakers, and beyond – but the current interest in them is due to the vast amounts of data now being generated and the need to process and understand it. They are now integrated into our lives. On the one hand, they are good because they free up our time and do mundane processes on our behalf. The questions being raised about algorithms at the moment are not about algorithms per se, but about the way society is structured with regard to data use and data privacy. It’s also about how models are being used to predict the future. There is currently an awkward marriage between data and algorithms. As technology evolves, there will be mistakes, but it is important to remember they are just a tool. We shouldn’t blame our tools.”
The “mistakes” Parpas refers to are events such as the “flash crash” of 6 May 2010, when the Dow Jones industrial average fell 1,000 points in just a few minutes, only to see the market regain itself 20 minutes later. The reasons for the sudden plummet has never been fully explained, but most financial observers blame a “race to the bottom” by the competing quantitative trading (quants) algorithms widely used to perform high-frequency trading. Scott Patterson, a Wall Street Journal reporter and author of The Quants, likens the use of algorithms on trading floors to flying a plane on autopilot. The vast majority of trades these days are performed by algorithms, but when things go wrong, as happened during the flash crash, humans can intervene.
“By far the most complicated algorithms are to be found in science, where they are used to design new drugs or model the climate,” says Parpas. “But they are done within a controlled environment with clean data. It is easy to see if there is a bug in the algorithm. The difficulties come when they are used in the social sciences and financial trading, where there is less understanding of what the model and output should be, and where they are operating in a more dynamic environment. Scientists will take years to validate their algorithm, whereas a trader has just days to do so in a volatile environment.”