Predictive Policing Crime halts the process of societies’ evolution towards sustainable civilization. Societies are prone to fragmentation when criminal tendencies remain undeterred by the police. Law enforcement agencies implement law and order to protect the lives and liberty of citizens.
Police are also responsible for diagnosing crime to prevent its transformation into different forms. For that, police remain responsive and proactive rather than merely reacting to crime. Therefore, predictive policing has emerged as a new strategy adopted by law enforcement agencies to anticipate the mutation of crime.
There needs to be a solid definition of predictive policing; instead, it is a framework that makes police efficient in intercepting crimes that will happen in the future. Crimes vary according to social settings and geographical locations. Predictive policing involves deploying resources to predict crimes in areas identified as potential hotspots.
One of the limitations of predictive policing is that it cannot determine criminals; it can only choose the crime and its possible place of occurrence. Police officers can prevent the commission of crime by exercising strict oversight in risky areas.
In simple words, Predictive Policing gives a number to police officials about the person that estimates how likely he is to break the law. Predictive Policing relies on machines to predict crimes.
Traditionally, the role of investigation units in law enforcement agencies is to counter the probability of crime. Surveillance computers gather masses of data about potential crime scenes.
The target of automated machines is to find the link between previous incidents that occurred at a specific time or place. The conclusion drawn from the analysis of data recommends resourcing police officials to expand their presence on those sites.
Modern surveillance machines used by police contain such algorithms that are instructed to scan criminal and personal histories of individuals for risk factors.
These algorithms provide personalized details, such as who was arrested and how often, and produce the details about the areas where there is the possibility of a threat to law and order situations.
Over the last decade, the use of predictive policing has spread all around the world. Police in over 50 countries, from Western democracies like France to authoritarian nations like China, are deploying these sophisticated tools in policing procedures.
The supporters of predictive policing claim that technology can help build a society where crime is nipped in the bud. Proactive policing prompts the police to patrol alleged hotspots.
However, it is imperative to understand that if predictive policing is not optimized, it can often target the most vulnerable members of society, pushing them even further to the peripheries.
Predictive policing helps deter organized crimes, but habitual criminals who do not commit crimes rationally will be considered to have severe problems maintaining law and order. Emphasis will be put on punishing offenders when data-driven programs identify them as recidivists.
Another existing issue with predictive policing is that the tools used in the prediction of crimes usually flag low-income communities as the epicenters of the grooming of criminals.
In developing countries, crime is perceived as the battle of have-nots against haves due to unequal distribution of opportunities and concentration of wealth. Therefore, predictive policing may focus more on low-income people as potential offenders.
It is essential to understand why predictive policing tools tend to replicate biases. The existing police data embeds a lot of historical discrimination. Data obtained through predictive analysis help police officers solidify their stance on suspects by drawing patterns of crime that are not always realistic.
Predictive policing is harmful if used only for profiling areas and criminals. It will become easier for police officers to avoid the hectic procedures of investigation as they will depend solely on data analytics to prove suspects guilty in those crimes in which they have no involvement.
In countries like Pakistan, police officers must be educated and informed enough to differentiate the information generated through machines. Technology is recognized as an error-less source of creating solutions.
Also, the judicial system in these countries still needs to be updated to counter the details provided by the police as proof in some instances. Predictive policing may not differentiate suspects from culprits.
For fair use of technology, training of police officers is mandatory to obtain accurate results that are both data-driven and realistic.
Predictive policing helps prevent heinous crimes like rape and sexual harassment in public places. Police officers can apprehend rapists by matching patterns and places where women remain vulnerable to their nefarious intentions.
The outcome of predictive policing in a positive manner can be materialized only when it is adjusted to human-sensitive information. Therefore, community-led policing is essential to integrate with machines and computers.
Every society has different dynamics, and so does the sensitivity of certain crimes. Somewhere, deviance is understood as breaking the law, whereas in other places, it may have only moral implications. Policing is a compassionate responsibility of the state.
The Blackstone ratio in criminal law states, “Ten guilty persons should escape than that one innocent suffer.”
Hence, Predictive policing must be capitalized as assistance only for curbing the menace of crimes. The integrity of human conscience must be preserved while depending unquestioningly on facts and figures.
The writer is a Punjab Police Officer in Bhakkar and can be reached at: Ammarsaleem797@gmail.com.