Hello World: How to be Human in the Age of the Machine

by Hannah Fry

Barwell was a 42-year-old married man and father of four, who had been in jail for armed robbery during the hiatus in the attacks. He now worked as a lorry driver and would regularly make long trips up and down the country in the course of his job; but he lived in Killingbeck and would often visit his mother in Millgarth, the two areas highlighted by the algorithm.11 The partial print on its own hadn’t been enough to identify him conclusively, but a subsequent DNA test proved that it was he who had committed these horrendous crimes. The police had their man. Barwell pleaded guilty in court in October 1999. The judge sentenced him to eight back-to-back life sentences.

And so PredPol (or PREDictive POLicing) was born.

You might well have come across PredPol already. It’s been the subject of thousands of news articles since its launch in 2011, usually under a headline referencing the Tom Cruise film Minority Report. It’s become like the Kim Kardashian of algorithms: extremely famous, heavily criticized in the media, but without anyone really understanding what it does.

Think of the algorithm as something like a bookie. If a big group of police officers are crowded around a map of the city, placing bets on where crime will happen that night, PredPol calculates the odds. It acts like a tipster, highlighting the streets and areas that are that evening’s ‘favourites’ in the form of little red squares on a map.

But the algorithm eclipsed everyone. In LA it correctly forecast more than double the number of crimes that the humans had managed to predict, and at one stage in the UK test, almost one in five crimes occurred within the red squares laid down by the mathematics.32 PredPol isn’t a crystal ball, but nothing in history has been able to see into the future of crime so successfully.

You could warn local residents that their properties are at risk, maybe offer to improve the locks on their doors, maybe install burglar alarms or timers on their light switches to trick any dodgy people passing by into thinking there’s someone at home. That’s what one study did in Manchester in 2012,33 where they managed to reduce the number of burglaries by more than a quarter. Small downside, though: the researchers calculated that this tactic of so-called ‘target hardening’ costs about £3,925 per burglary it prevents.

That means there is one very big potential downside of using a cops-on-the-dots tactic. By sending police into an area to fight crime on the back of the algorithm’s predictions, you can risk getting into a feedback loop.

PredPol is not the only software on the market. One competitor is HunchLab, which works by combining all sorts of statistics about an area: reported crimes, emergency calls, census data (as well as more eyebrow-raising metrics like moon phases). HunchLab doesn’t have an underlying theory. It doesn’t attempt

Another opaque predictive algorithm is the Strategic Subject List used by the Chicago Police Department.40 This algorithm takes an entirely different approach from the others. Rather than focusing on geography, it tries to predict which individuals will be involved in gun crime. Using a variety of factors, it creates a ‘heat list’ of people it deems most likely to be involved in gun violence in the near future, either doing the shooting or being shot.

And in the UK, cameras mounted on vehicles that look like souped-up Google StreetView cars now drive around automatically cross-checking our likenesses with a database of wanted people.50 These vans scored their first success in June 2017 after one drove past a man in south Wales where police had a warrant out for his arrest.

That’s because the UK police now hold a database of 19 million images of our faces, created from all those photos taken of individuals arrested on suspicion of having committed a crime. The FBI, meanwhile, has a database of 411 million images, in which half of all American adults are reportedly pictured.

There’s good reason for stores to want to use this kind of technology. An estimated 3.6 million offences of retail crime are committed every year in the UK alone, costing retailers a staggering £660 million.71 And, when you consider that in 2016 there were 91 violent deaths of shoplifting suspects at retail locations in the United States,72 there is an argument that a method of preventing persistent offenders from entering a store before a situation escalates would be good for everyone.

This was precisely the idea behind a famous experiment conducted by Matthew Salganik, Peter Dodds and Duncan Watts back in 2006 that created a series of digital worlds.1 The scientists built their own online music player, like a very crude version of Spotify, and filtered visitors off into a series

The results were intriguing. All the worlds agreed that some songs were clear duds. Other songs were stand-out winners: they ended up being popular in every world, even the one where visitors couldn’t see the number of downloads. But in between sure-fire hits and absolute bombs, the artists could experience pretty much any level of success.

over 0.8. Sreenivasan’s study showed what social scientists had long suspected: we’re put off by the banal, but also hate the radically unfamiliar. The very best films sit in a narrow sweet spot between ‘new’ and ‘not too new’.

They discovered a connection between the number of edits made to a film’s Wikipedia page in the month leading up to its cinematic release and the eventual box-office takings.12 The edits were often made by people unconnected to the release – just typical movie fans contributing information to the page. More edits implied more buzz around a release, which in turn led to higher takings at the box office.

Here’s how Kasparov puts it: ‘When playing with the assistance of computers, we could concentrate on strategic planning instead of spending so much time on calculations. Human creativity was even more paramount under these conditions.’2 The result is chess played at a higher level than has ever been seen before. Perfect tactical play and beautiful, meaningful strategies. The very best of both worlds.