Algorithms and analytics should be just as much a part of law enforcement as paperwork and patrol cars. Chris Bull explains why push analytics, together with transparency and ethics, should guide the way forward
Data. Rarely does one word convey such global significance. Think about it. Pretty much everything we do these days is either creating or being shaped by data – and that’s even before we reap the full impact of advances such as artificial intelligence, drones and many others.
No sector or organisation is immune to the scale of this revolution – that’s what it is, a revolution – and this includes our law enforcement and security agencies. They have identified the challenge of trying to spot relevant information within a wealth of fragmentary data as one of the most pressing priorities of our time.
On the plus side, new technology can bring a wealth of key information to stretched and overburdened police forces. But how can the police harvest and deploy this key intelligence, particularly when current analogue methods of acquiring and analysing data are too slow and time consuming at a time of limited resources?
It’s interesting that the use of technology in law enforcement has drawn both critics and supporters. Although it has huge potential to improve our lives and the services we rely on, not everyone is convinced. Indeed, the public appear increasingly nervous, particularly in regard to use of information by the state.
You can see why many may harbour some doubts. For example, my colleague, Mivy James, recently highlighted some deep-rooted issues with bias in the facial recognition technologies that are being adopted by police forces around the world, particularly those in the US.
Similarly, there is a concern that police may simply be overwhelmed by data. Combine huge volumes of digital evidence with insufficient numbers of police officers possessing the necessary skills to process it and it’s clear that we risk missing out on technology’s huge potential to help bring down crime.
That’s why we need to pivot towards ‘push analytics’ to help address these challenges. These use varieties of automation, rule based alerts, and machine learning algorithms to do some of the heavy lifting of information triage and the prediction of future threats, highlighting which lines of enquiry to focus on.
For example, a 2018 RUSI study of predictive analytics in policing indicated that machine learning algorithms were significantly more effective in predicting the locations where crime was likely to occur than existing intelligence-led techniques. This prompted the UK’s Durham Constabulary to develop its own machine learning tool to try and predict individuals who are more likely to conduct criminal activity.
Charting an ethical future
It’s important to reiterate, however, that transforming investigative and analytical capability does not come without risks. Algorithms can be biased as a result of the data used in their creation, and in the sensitive fields of crime prevention and terrorism, where some ethnic and religious groups already feel they are unfairly singled out by the authorities, getting this wrong could hugely impact public trust.
So it’s not just a case of picking the next available technology. Cutting edge digital advantages may inspire awe but without citizens’ confidence that they will be used appropriately they will fall short of their potential – particularly when it comes to law enforcement. Policymakers therefore have to respond to – not ignore – the genuine concerns of those worried about the misuse of advanced technologies and who want to have their privacy protected but also feel safe and secure.
To do this they should heed the advice of my colleague, Holly Armitage by prioritising data ethics and be transparent about how they are doing this. At the same time, they need to develop the operational capabilities needed for this new age and ensure they are effectively embedded within their organisational processes. Digital skills for new police recruits, for example, should be a pre-requisite.
Striking this balance won’t happen overnight but will, in time, unlock the full potential of the data revolution as it accelerates into the future.
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