The practice of using Artificial Intelligence and Machine Learning to track the identity of perpetrators is now many years old. Both AI and ML hold the potential to not only reveal the identity of criminal but his/her whereabouts at the time of incident and actions both prior and following the incident.
US Law Enforcement strongly believes that AI will soon help them foresee crime. They would be able to thwart violent and criminal events. However, the technology is in the early stages, but the growth rate is sufficient to keep hopes high.
The tasks like probing activities of suspected criminals are strenuous if done manually. Artificial Intelligence makes it possible to analyze massive amounts of visual data along with Machine Learning behavior scripts. Therefore, it can eliminate human errors, especially when it comes to witnessing identification and boosting arrest accuracy.
In this blog, we will check out three ways Artificial Intelligence and Machine Learning can help in crime detection.
1. Collection of Public Data Can Thwart Brutal Crime
The practice of identifying date, times and locations where the probability of particular criminal activity is higher is called, ‘Predictive Policing’.
It also includes keeping surrounding of those vulnerable areas safe by scheduling officers to patrol those areas.
Predictive analysis models have been continuously refined with the help of research and input from police departments and software suppliers.
Investigations have shown that the three most important parameters are following.
- Date and Time of Crime.
- Location of Crime.
- Type of Crime
It is very easy to build a profile matrix of suspected. Database, which contains known associates, DNA found and gunshot detection can help. Videos from security cameras and traffic light increase the chances of accurate arrest.
It is deplorable that some communities are excluding traffic light cameras. It would prevent law enforcement from getting possible conclusive data. The key to foresee the crime is continuously getting consistent data.
2. Crime is Pattern, Not Random
Years of research have demonstrated that there is no such thing as random in crime. That is the crux of forensic analysis history.
“There exist crime-place networks” so says David L. Weisburd. He is an Israeli American criminologist known for his research on the crime-placement theory and policing. He along with his peers confirms that criminal activities always follow patterns. The patterns might be traced in places, victims and offenders.
Law enforcement can collect and store massive amounts of data, apply complex analytics models and can map patterns of possible criminal hotspots through analysis. Forensic departments support deployment for high crime places. They take initiatives to protect high-risk victims and ensure possible deterrents for repeat offenders.
This is the part of modern policing to schedule patrolling officers with help of maps, which show spots prone to crime.
The mechanism of reading crime behavior patterns goes like this. Key performance indicators, which commonly are typical place scene features, act as input. They are used in conjunction with historical crime reference data, which includes images of reoccurring criminal places. We get crime enabling behavior patterns as output.
The “Risk Terrain Modeling” framework illustrates the design matrix based on risk analysis of crime possible in the future. It is developed by Leslie Kennedy, Joel Caplan, Eric Piza, Grant Drawve and the team at Rutgers Center on Public Security (RCPS) in New Jersey, America.
The Loss Prevention Research Council (LPRC) Violence Crime Working Group and its Anti-Violence Innovation Team have made software and these analytics tools in hopes of getting ahead of spatial violence perpetrated against schools, arenas, churches, government buildings, etc.
Their ultimate goal is to protect employees and the public from unthinkably violent offenders. It is definitely not so difficult to achieve if you position police at the right place and time before violence occurs.
AI algorithms have also been proven effective in medical sciences as much as forensic sciences. It includes DNA analysis and radiological image interpretation. Both of them are used to determine the cause and manner of death more accurately.
3. AI/ML Gets More Human Due to Layers of Neural Networks
As neural networks are on big data, machine learning and big data are utilizing affordable and fast graphical processing units. Projects seek to come up with safety systems capable of continuously learning from big data analytics, as it executes tasks while retaining their current abilities and improve decision making. One such example is Lifelong Learning Machines (L2M), from the Defense Advanced Research Projects Agency (DARPA).
It brings researchers on one page, to discover and understand how learning happens biologically and work on the development and enhancement of computational architectures and algorithms.
These machines imitate human learning mechanism. They go through different phases of evolution as they strive to execute tasks in more puzzling situations. Layers of neural networks make this all possible.
Are the statistics hopeful?
Yes, they are. Major decline in crime rate has been witnessed in the past decade, in the countries where predictive law enforcement is taken on serious notes. This is proof that investment in predictive law enforcement is successful and worth making. Artificial Intelligence and Machine Learning have bluntly demonstrated their powers in crime detection and prevention.
So in short, the picture is beautiful. AI and ML can be unbelievably helpful in the current international scenario, where many countries have restrained budgets.
We hope the article was eloquent enough to give you a better understanding of what predictive law enforcement is. You are welcome to give feedback in the comments section.