Using Artificial Intelligence to Turn Big Data Into Actionable Intelligence for Police Forces

Picture a city where surveillance security systems and law-enforcement technology identify a suspect from a live urban-monitoring feed, helping to dispatch officers to the right area and arrest the person in minutes. Elsewhere, a police officer is alerted to a tourist in distress. Unable to understand her concerns, he uses a translation tool to interpret her words in real time and provide the right support. Meanwhile, a citizen drops into an unmanned kiosk to report antisocial behaviour on public transport during her commute home. 

These are just a few examples of the ways in which police departments are using AI applications or big data to serve the public. What is big data? It’s vast datasets that can reveal trends, patterns or irregularities when analysed by computers. Both AI and big data are being adopted by police forces around the globe as tools to streamline services and improve investigative and preventive policing. 

However, there are also challenges in handling vast amounts of data and converting them into actionable information, as well as problems integrating this new technology into policing. Here, we explore both the challenges and benefits of adopting AI and big data technologies into law enforcement. 

Policing in a Data-Driven World

While there are great differences in how police use artificial intelligence between nations, regions and cities, and even within departments, there are three core uses of AI, machine learning and big data within policing:

• To automate operations
• To improve interactions with the public
• To enhance investigations and decision making 

Let’s explore these.

Using Intelligent Automation Processes 

Intelligent automation process technology effortlessly performs repetitive tasks. Principally, these are office tasks like form filling, data cataloguing, redacting documents and various back office and business support tasks. There are also opportunities to use AI for various other station management tasks, such as operating specialised security systems and car park management. 

Using intelligent automation processes has the potential to improve efficiency in policing and make significant cost savings. 

Using AI for Police Public-Facing Tasks

Chatbots are a core example of how police can use AI to handle incoming enquiries. Chatbots can be trained to handle the reporting of non-emergency issues like property damage or theft, freeing human resources to handle reports of crimes in progress. AI can also aid policing in real-time translation.

Dubai’s smart police stations, where the public has 24/7 access to automated, human-free kiosks to file reports, access resources and apply for certifications in their choice of seven languages, are one of the most progressive examples of AI for public-facing police work. 

Using Big Data to Enhance Preventive and Investigative Police Work

It is within hands-on investigative and crime prevention work that AI and big data have the most potential to create powerful solutions for modern policing challenges. AI tools have the ability to work faster, more efficiently and more accurately than humans in analysing vast amounts of data. Here’s how AI is used to speed up and improve both preventative and investigative police work. 

Predictive Policing

Predictive policing uses big data and data analytics to risk assess and predict where crimes occur, helping to direct police resources appropriately. Big data can also be used to assess the risk of a convicted person reoffending or the risk of a vulnerable person becoming a victim of crime, meaning that it’s important to crime prevention strategy. 

Automatic Crime Detection

AI is reducing the time spent on detecting crime, particularly traffic offences. AI-powered cameras can identify speeding vehicles and drivers in violation of seatbelt and mobile phone laws, freeing officers from roadside duties. 

Digital Forensics

Facial recognition software uses AI to check images against vast databases to screen for wanted persons, either in real-time or retrospectively. Facial recognition AI technology can draw on images or videos taken from commercial surveillance systems, dashcams, smartphones, social media, and even video doorbells to help find and accurately identify individuals. 

AI technology can also be trained to review vast amounts of video footage to identify suspicious behaviour, possible weapons, vehicles, or other of-interest objects or activities.

Digital data processing tools can be used to analyse vast amounts of text data, such as emails and texts, to identify patterns or unusual behaviour and provide crucial evidence.  Through using AI tools for keyword detection and conversation analysis, police can also monitor social media to collect evidence on individuals or criminal networks. 

The Current Challenges of Using Big Data in Policing 

While police forces are increasingly using AI and data-driven policing solutions, various challenges remain. Using vast datasets to extract actionable information is still not as efficient as it could be for various reasons. Challenges faced by law enforcement agencies around the world could include:

• Encouraging the embrace of technological advancement within police culture
• Poor quality or inconsistent data locked into legacy systems
• Organisational structure and cross-departmental hindering efficient use of AI and big data
• Ethics and bias concerns
• Changing threats from the criminal world
• Laws and regulations that are slowing innovation and focus on outdated practices
• Insufficiently or inappropriately skilled workforces
• Budget restraints. 

AI and Big Data in Policing and the Wider World

The adoption of AI and big data is fundamentally changing policing. It’s adapting processes in crime prevention and detection, forensics and investigative work, as well as how a police department manages its facilities, administration and public-facing duties. Outside policing and in the wider world. AI is being integrated into more and more applications and systems, too, improving efficiency, functionality, and value for money. 

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