You arrive in London for a dream vacation in one of the world’s great cities. After a quick stop at the cash machine, you head to the historic Gordon’s Winebar, the oldest such establishment in the metropolis. After a delicious meal you turn around to discover that your favorite leather jacket, with your credit cards and the 200 pounds you just withdrew from the cash machine in the pocket, has disappeared from your chair.

The wine bar is crowded, loud and full of shadows. You didn’t observe what happened. Did the thief steal your jacket when you were in the loo, or did your jet lag make you nod off for a moment and give him a chance to swipe it almost literally off your back? And is the crook now using your credit cards and money? All you know for sure is that your vacation has just been ruined.

You could of course telephone the police and make a report, but even if they have the time to track down a jacket and 200 quid in a city the size of London, it would be a few days before they showed up to interview any witnesses. At that time, the wine bar could physically hand over all the film from its closed circuit cameras, but the officers may not have the resources to plough through hours of footage for a petty theft case when they have so many other demands upon their time.

Or at least that’s the way it used to be, before the remarkably clever chap who owns the wine bar invented a crowdsourcing method of policing petty theft, vandalism and the like.

Simon Gordon founded Facewatch, a web based system that is free to all businesses and police constabularies. In the above scenario, the crime victim and the business owner would use the Facewatch software to enter the crime report on line along with the most relevant film footage they could find (i.e., the best image of the person who stole your jacket). The police would receive the evidence immediately rather than days later, thereby getting a head start on cracking the case. And their marginal cost to investigate would be dramatically lower because the business owner and victim have done the upfront legwork for them.

In addition, other businesses in the area of the theft would get a copy of the still images of the suspect, helping them protect themselves from the same perpetrator. As a victim, you would get peace of mind because your stolen credit cards could be cancelled using the software, and, you would receive regular updates by email about the progress of the case (A common complaint of crime victims, and one which exacerbates their emotional upset, is that police don’t keep them informed as to whether anyone is pursuing their complaint, and with what success).

Facewatch is a new technology used by a minority of businesses, but it has already resulted in the arrest and conviction of some serial thieves. That’s a good thing, but the best law enforcement tools prevent crime rather than simply detect it. It’s early days yet, but it seems reasonable to assume that if the Facewatch technology is widely adopted, it could have some deterrent value for pickpocketing, shoplifting and vandalism.

Mark Kleiman has speculated about whether policing could be crowdsourced to ordinary citizens, for example people with mobile phone cameras who happen to witness crimes. That seems worth a try, but many citizens may not be particularly motivated to report a petty theft that happens to someone else. In contrast, businesses have a standing interest in not having their property and customers robbed, and with CCTV on site, they also have more infrastructure available to help the police investigate.

A rigorous scientific evaluation of Facewatch has yet to be done, but its inventiveness is undeniable. Like a lot of other people who are interested in reducing crime, I will be monitoring its potential impact as it spreads throughout the UK.

[Cross-posted at The Reality-Based Community]

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Keith Humphreys is a Professor of Psychiatry at Stanford University and served as Senior Policy Advisor in the White House Office of National Drug Control Policy in the Obama Administration. @KeithNHumphreys