Data Driven Policing and the Public Mood
Police forces all over the world are under mounting pressure to come to terms with new technology enabled forms of crime. If they fail to respond adequately, they may face a catastrophic collapse in effectiveness, as criminals slip beyond the reach of the law. Where police forces do respond, taking advantage of what technology can offer in terms of improved crime prevention and detection, they run the risk of using tools the public do not understand and are suspicious of.
Both not doing enough and doing too much too quickly could therefore lead to a serious loss of public confidence and trust in policing. In seeking to navigate a path between each of these extremes, improved efforts to understand and facilitate a role for the public voice are likely to be vital.
That criminals are being creative in their use of technology is beyond doubt. Examples abound of crypto-currencies being used to facilitate money laundering, the trade in contraband goods, the funding of terrorist activities, and tax evasion and extortion. Analysis from the University of Technology in Sydney found that a quarter of bitcoin users, and a half of all bitcoin transactions, were associated with illegal activity. In 2017, that amounted to an estimated transaction value of $72bn.
Elsewhere, with an estimated 31 billion devices projected to be connected to the internet worldwide by 2020, and 75 billion by 2025 and with many manufacturers failing to embed even basic security measures into their devices, the Internet of Things also represents a huge opportunity for criminals. They have noticed. The Mirai Botnet Distributed Denial of Service (DDoS) attack that brought down major sites like Twitter, Paypal, Netflix and Facebook in October 2016 was facilitated by the hi-jacking of millions of devices, including CCTV security cameras and baby monitors, with software that commanded them to attack and overwhelm the targeted servers.
At a more prosaic level, Mike Barton, the Chief Constable of Durham Constabulary in the United Kingdom has warned of the IoT leading to a ‘crime harvest’. “If your fridge is connected up to your local supermarket so that it can order things when they are needed,” he said, “then it’s going to be connected to your bank account and it’s that, that is the worry. That all of these devices, none of which are seen as that threatening or that necessary to protect, become the open back door.”
More seriously, security vulnerabilities in the IoT offer new opportunities to commit murder or political assassination. The former Vice President of the United States, Dick Cheney, confirmed in an interview with CBS as far back 2013 that his heart pacemaker had had its wireless function disconnected to prevent a possible assassination attempt by hackers.
On the other side of the coin, the police are finding novel ways to prevent and detect crime. When police started using distributed gunshot detection sensors in New Jersey, for example, they found that 38 percent of gunshots in one neighborhood were not being reported at all. This enabled them to focus more resource on that area than previously had been the case.
In Vancouver, the Police Department has implemented a city-wide predictive policing tool to target property crime. The system uses machine learning and both historic and current crime data to predict where break-ins are likely to occur. It pushes that information to the onboard computers of patrol vehicles at two hourly intervals so officers can alter their patrol locations with a view to preventing it. A six-month pilot project in 2016 saw property crime reduced by as much as 27 per cent in areas where it was tested, compared to data held on the previous four years.
There have also already been cases where Fitbit and heart pacemaker data has been used to help build cases against murderers and arsonists, as the crime scene not just of the future but of today contains a large digital footprint relevant to investigations.
Looking ahead however, to be effective the police will most likely need to start using tools that will automate aspects of their investigations. Some senior police leaders have already warned that without use of artificial analysis (AI) tools to analyse and assess digital evidence, they will simply be overwhelmed by the amount of data available, with important pieces of information being missed. Will the public be content with software programmes effectively deciding which crimes will be investigated, and within that, which lines of inquiry will be pursued?
Predictive systems are already in use in some custody suites too, helping officers to make decisions about which offenders are likely to offend again and ought therefore to go to court to face a possible prison sentence and which can be directed into community or other programmes. The problem is that even the coders of the software often cannot explain exactly how the machine learning algorithms have arrived at a particular prediction, meaning it is hard for the subject of to understand or challenge it.
These developments amount to fundamental changes to both the procedures of justice and accepted principles related to access to it. This is before one even considers the implications of ever-growing quantities of personal data being made available to the police and the possible implications for privacy that this involves. Some jurisdictions are attempting to manage this whole area through the formation of privacy and ethics commissions made up of policing, technology and ethical experts. This, however, is an attempt to fix what will be a public confidence issue through better use of expertise.
The big gap in the entire enterprise is the lack of structured opportunities anywhere for the public to get involved with the issues and trade-offs and to express a view. If that does not change soon, it is likely to be sudden shifts in public mood driven by particular high-profile cases, rather than measured public debate that will prove decisive in shaping the battle for law and order in the digital age.
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