Artificial Intelligence in Law Enforcement - Part 2
In the first part of the article on AI use in Law Enforcement, we introduced how Machine Learning and Artificial Intelligence is being used by the law enforcing agencies around the world and what are the pros and cons of using this emerging technology. Now let us discuss different ways by which the police can use machine learning to achieve better results:
1) Pattern recognition
One of the most powerful applications of machine learning in policing is in the field of pattern recognition. Crimes can be related and may either be carried out by the same person(s) or may use the same modus operandi. The police can benefit if they are able to spot patterns in crimes. The data that the police get from crimes is essentially unstructured data. This data needs to be organized and sifted through to find patterns. Machine learning tools can compare various crimes easily and generate a similarity score. These scores can then be used by the software to try and determine if there are common patterns.
2) Traffic Management and Automatic Detection of Violations
The centralized traffic management centres can use machine learning algorithms instead of police officers manually viewing large amounts of videos and camera feeds to automate the traffic management at major signals. The same can be used for detecting the traffic violations and sending the automated challans to the violators.
3) Facial Recognition of Criminals
Use of machine learning in surveillance systems can help to recognize faces and this can help in solving and preventing crimes as well as intercepting the criminals. These surveillance systems can be installed at airports, railway stations, major public areas to help the police identify and arrest criminals based on an image that is fed into the system.
4) Predictive analytics
Another area related to machine learning that can help police is predictive analytics. This is a powerful application of machine learning that the police can use to achieve effective results and help police in improving public safety. The ML tools focus on crime trends and are thus beneficial. When such trends are spotted, the police can proactively take action. For example, when the system identifies a trend in a crime being committed in a particular area, the police can then allocate resources to that area so that they can proactively manage the situation and prevent a crime from occurring.
Cybersecurity is an important area in today’s world. With the extensive use of internet everywhere, cybercriminals are targeting computer systems across the world. Cybersecurity is very important to not just to solve cases but to proactively prevent them. Cybersecurity can be enhanced using machine learning. Tools that use machine learning can improve cybersecurity and proactively prevent crimes.
6) Enhanced public safety
Ensuring public safety is an important function of the police. This can be enhanced through the use of machine learning. Some tools that are being used in this regard are:
- Gunfire detection sensors can detect instances of gunshots and triangulate the location. This can be done effectively through machine learning. Police can reach the trouble spot quickly even before anyone can call or raise a complaint.
- A machine learning-based system can even predict if a person will commit a crime based on past data and trends.
- Crowd control through analysing images and videos of people entering or exiting a demonstration venue
All these developments will aid the police in their work and help them achieve better efficiency. We already have a disparity between the number of police personnel required vs actual at any given point of time. Adopting some of these technological advancements and implementing artificial intelligence judiciously will go a long way in making the police force more systematic and effective.