Artificial Intelligence is often associated with machine learning algorithms, making predictions, or designing new digital (or non-digital) products. However, AI professionals can make an impact on the social good. No matter where you live, crime is an all-pervasive problem that permeates every society.
It’s an era where everything we do is recorded and stored on multiple servers around the globe, regardless of whether an individual or group is attempting to steal property or identify. Law enforcement agencies have access to a wealth of data, provided that the social media giants and other data collectors (including those who might record crimes via their phones) agree not to share it.
The US has noticed a very steady decline in the number of property crimes and violence  since 1990. Similar decreases in crime over the past 17 years have been documented by the UN’s 2017 global crime report .
The fact that criminals are using the same tools (or similar) as non-criminals, such as smartphones, social media, etc., and the machine learning/AI algorithms becoming more sophisticated in their nuanced detections of criminal activity or fraud, should not be surprising. There is more to be done with data science tools and processes that identify a threat before the criminal (or any thief) can execute their plan.
Law enforcement agencies were a fragmented data-sharing network in the past. This situation has changed with the introduction of national databases such as the Federal Bureau of Investigation’s National Crime Information Center  and crime mapping software. Internal organizational management tools like CompStat and OneDOJ  are good examples of crime detection tools.
AI professionals and data scientists in law enforcement might be assigned to merge information from social media with data from their internal database. It may not require data scraping experience, depending on whether someone is already skilled at this task. This caution is meant to make sure you are building a reliable crime detection profile.
Also, maintain the ability to discern between the various degrees of circumstantial evidence. People will post strange things to social media or run Google searches that law enforcement could use to identify criminal intent. As is often stated, not all data points are available—for example, text analytics of transcripts that contain victim or witness statements. You can know much more about crime detection through AI with the help of an online artificial intelligence course .
When using artificial intelligence and big data, international law enforcement agencies face various operational problems with the police. To identify potential threats, you can gather vast amounts of data and information from surveillance gadgets and the internet. Deep learning-based artificial intelligence security systems can use image recognition to recognize weapons in real-time video footage. Machine learning detects hidden weapons as people pass through doors and other entrances.
Law enforcement agencies can also use artificial intelligence to find clues and people of interest in videos. AI can search for objects and features in videos to identify and track suspects. Face recognition technology can be used in real-time to identify criminals using surveillance footage. These AI systems can process vast amounts of data per second without human intervention and identify objects with high accuracy.
Suppose you decide to pursue a PG in AI and machine learning. In that case, you can know how agencies such as Interpol, the largest international law enforcement agency, use AI to identify criminals. Social media activity can be monitored using machine learning algorithms to identify criminals and possible threats. Combining these technologies can aid in creating a foolproof surveillance network that can assist in peacekeeping operations and track people of interest.
The significance of big data and AI is being recognized by international law enforcement agencies, who are gradually incorporating these technologies into the operations of their organizations. Law enforcement agencies are slowly adopting these technologies as criminal organizations do. Therefore, law enforcement authorities need to up their game and accelerate the adoption of AI.
It can be challenging to analyze video evidence. Everybody has a smartphone-based camera. In the United States, police officers are increasingly using body cameras. During an investigation, hours upon hours of footage are reviewed. Inclement weather and malfunctioning body cameras can cause poor video quality. Data scientists can help shorten video reviews by using AI to identify “a zone within the video frame where any movement causes alert to generate.” The frame can then be tagged to allow for further analysis.
Data scientists can also help law enforcement identify criminals who are not yet detained under outstanding warrants by facial recognition. While body cam AI is still being developed and refined, facial recognition algorithms can be used by police officers to notify them immediately if they’re in the presence of a criminal with a warrant for their arrest.
Multiple law enforcement agencies have tried predictive police, including the UK police in Durham . Harm Assessment Risk Token is a classification system that ranks individuals according to the likelihood that they will be convicted of another offense in the future.
This system used data from 2008 to 2013 and rated people according to their current crime severity, criminal history, flight risk, and other factors. Hart’s forecasts were accurate in a large percentage of cases. Still, other studies caution against using predictive software and algorithms because they flag minorities as at greater risk than white defendants. ProPublica  shows how human bias is introduced into these formulas due to the flawed judgments of humans.
The research into improving predictive accuracy continues. Researchers, machine learning engineers from different industries, data scientists, and AI enthusiasts continue to improve machine learning, deep learning, and other AI tools for various purposes. A new application area is where algorithms and human decision-making can work together, but you should rely on both components too much.