Throughout the ages, humans have endeavoured to devise novel means to improve their lives. Every once in a while, a revolutionary invention comes with the power to advance humanity and reshape the trajectory of history.
From the wheel to the World Wide Web, there have been many inventions that resulted in enhancing the efficiency in the work we do.
A notable technological advancement in recent times is the advent of Artificial Intelligence (Al), which is changing the way we work and live. We see numerous instances of how Al is affecting society and impacting our lifestyle.
AI and its sub-system machine learning (ML) have already been part of various sectors, such as transportation, finance, energy, healthcare etc., for a fairly good amount of time. However, the incorporation of AI into police forces is a more recent development compared to its longstanding presence in other domains.
We have witnessed that in most cases malicious actors are the pioneers in adopting any new technology. AI is already leveraged by them to script and automate crimes.
It is crucial for law enforcement agencies to be fluent with contemporary technologies for better justice delivery.
Artifical intelligence has now become a pivotal tool to prevent and detect crimes, especially in urban areas. Although AI in policing is still in the early stages in India, but there is gradual integration in the following ways:
1. Facial recognition of criminals: The integration of machine learning into surveillance systems has proven to be valuable in recognising human faces and this can help in preventing and detecting crimes in addition to intercepting criminals. These surveillance systems can be installed at airports, railway stations, and major public areas to help police identify and arrest criminals based on images fed into the system.
2. Predictive analytics: Another area related to machine learning that can help police is predictive analytics. Predictive analytics that leverages machine learning is a powerful tool that police can use to improve public safety measures and achieve operational efficiency.
Machine learning tools focus on patterns and trends in historical crime data, making it easier for police to anticipate where and when such crimes are most likely to occur. When such trends are spotted, police can proactively take action by allocating necessary resources and concentrating their efforts.
For example, when the system identifies a trend in a crime being committed in a particular area, police can then allocate resources or increase patrolling to that area so that they can proactively manage the situation and prevent a crime from occurring.
3. Pattern recognition: One of the most powerful applications of machine learning in policing is in the field of pattern recognition. Crimes often exhibit distinct patterns, whether carried out by the same individuals or those who share a similar modus operandi.
Police can benefit if they are able to spot patterns in crimes. The data that police get from crimes is essentially unstructured data. This data needs to be organised and sifted through to find patterns. Machine learning tools can compare various crimes easily and generate a similarity score. Machine learning’s ability to transform raw and unstructured crime data into actionable intelligence can lead to more effective crime prevention.
4. Cybersecurity: With the extensive use of the internet everywhere, cybercriminals are targeting computer systems across the world. The importance of cybersecurity goes beyond solving cases: it is crucial for proactively preventing cybercrimes.
Cybersecurity can be enhanced using machine learning for anomaly detection, behavioural analysis, phishing & fraud detection and many more. By harnessing machine learning capabilities, we can stay ahead of cyber threats and bolster our defence mechanisms.
5. Traffic management: The centralised 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.
Some of the traffic management and enforcement measures that imply/utilise artificial intelligence & machine learning are automated video analysis, dynamic traffic flow optimisation, traffic violation detection and automated challan generation.
Intelligent Traffic Management System (ITMS) launched by the Bengaluru Traffic Police is an excellent example of using AI in automated challaning system.
6. Enhanced public safety: Ensuring public safety is an important function of police. This can be enhanced through the use of machine learning. Some tools that are being used in this regard are:
Machine learning-powered gunfire detection sensors that can detect instances of gunshots and triangulate the location based on the audio data from the sensors. Police can reach the spot quickly even before anyone can call or raise a complaint.
A machine learning-based system can analyse historical crime data to predict the likelihood of commission of crime, potential crime hotspots and help police to prevent it.
Utilising advanced image and video analysis, AI-assisted crowd control systems can help LEAs in monitoring and overseeing large gatherings during events, protests or assemblies. Further, this can facilitate efficient management of crowds entering/exiting the venues, providing insights to ensure public safety.
By embracing contemporary technological developments and leveraging
AI judiciously, law enforcement agencies can bridge the gap between demand and availability of police personnel. All these adoptions can contribute to a more systematic, efficient and effective police force that enhances public safety and security.
(M A Saleem is Director General of Police, CID, Economic Offences and Special Units, Bengaluru)
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