When a social media site throws out an ad for a product you were just discussing over the phone, it’s easy to jump to conclusions: They must be listening, surely.
But the truth is that the site employed artificial intelligence (AI) to predict your behaviour. You searched for a yeast starter last week and commented on a friend’s photo of sourdough bread yesterday. The ad for a bread-making course that seemingly pops up out of the blue was shown to you because the data predicted you might be interested in it – based on your own and previous users’ behaviour.
Those same principles can be applied to fight crime – and soon will.
From road accidents to riots
When it comes to crime, it’s not quite as simple as scraping social media to find people who did successive searches for ‘crowbar’ and ‘balaclava’.
Data exists to predict, and thus prevent, crime – it’s just not being analysed as yet.
There is a wealth of information to predict the likelihood of crime. It spans from the obvious indicators – like a person’s presence in a bad part of town – to the surprising, which includes weather patterns and the days of the week.
A Finnish study, for example, showed that a 1°C increase in temperature results in a 1.7% increase in criminal activity – based on two decades’ worth of data. Another study in the US proved that vehicle theft spikes on weekends and in the evenings. And science has even shown that when a local football team loses unexpectedly, domestic violence incidents increase by 10%.
By collating all this data, and past crime statistics, information from tipping lines, social media scraping, CCTV, and more, crime can be predicted, and emergency services proactively dispatched before it’s too late.
The principle can also be used to predict spikes in traffic accidents and dispatch emergency services to nearby locations for even faster response times. The artificial intelligence employed is similar to Uber’s algorithms which predict when and where there will be a high probability of ride requests so they can dispatch drivers proactively.
So, when it comes to riots, this principle is the only practical answer. Riots – like the ones parts of South Africa was subjected to recently – are one of the most difficult emergency incidents to manage because they have such a staggering snowball effect.
History has shown that once a riot escalates past a certain point, almost nothing can be done that won’t be to the detriment of everyone involved. By predicting it, it can be prevented or contained in the early stages.
Using AI proactively
At the moment, emergency services – from ambulances to private security and police – largely operate on a reactive basis. A call comes in, and a vehicle is dispatched to assist. As the country’s crime and emergency statistics keep increasing, it’s clear that a proactive approach is the answer.
And it will happen soon: Predictability fuelled by AI and big data can reduce violent crimes by 25% as early as 2023.
Data engineers at Aura are already working on expanding its existing security and medical response algorithms, to become the central repository for risk data. Hundreds of informants will be employed to operate on the ground and send information to the repository. Augmented with pools of data collected from available and existing sources, this information feeds into a so-called data lake, where AI is applied to create the intelligence that can predict crime and other emergencies.
By combining the forces of AI, private and public security, crime can be fought in a collective, coordinated, and proactive way. Safety should be a basic human right, not a privilege. And now, it can be.