TECHNOLOGY IN LAW ENFORCEMENT 7
Running head: TECHNOLOGY IN LAW ENFORCEMENT 1
Technology in Law Enforcement: Predictive Policing
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Technology in Law Enforcement: Predictive Policing
Cybercrime and cyberterrorism remain significant threats in the age of technology as criminals find new avenues to use modern technology in their criminal activity. However, technology has also improved law enforcement through various implementations. One such implementation is predictive policing. The technology is an advancement of previous models of crime analysis and crime mapping technology. While technology has been a controversy concerning privacy, it remains a significant improvement to law enforcement in the modern age. The purpose of the current paper is to discuss the design and implementation of predictive policing in modern law enforcement and its implication to the sector, and the overall community. Although the technology poses a significant threat to privacy, it is crucial in implementing law and order in the modern age.
Predictive Policing
According to Perry (2013), law enforcement officers should invest in identifying and managing criminal activity before execution. While there is no universal definition of predictive policing, most researchers and agree on its main features. It involves various analytical techniques and technology to identify potential crime, offenders, perpetrators, and victims (Perry, 2013). Predictive policing focuses on the use of previous data to speculate on possible criminal activity. One of the main features of predictive policing is data mining. The process involves the use of advanced computing technology to collect related data concerning a particular topic, in this case, criminal activity. The analysis of such information is vital to law enforcement since it helps law enforcement officers understand the trends in illegal activity that, in turn, provides a basis for speculation, planning, and decision-making. Predictive analysis is, therefore, also essential to the implementation of predictive policing. Predictive policing is, therefore, useful in the prevention of crime.
Implications to Law Enforcement Organizations.
Predictive policing has significant strengths as a law enforcement tool. An excellent example of the potential benefits of its implementation helps ensure the accurate deployment of resources and personnel. According to Meijer & Wessels (2019), predictive policing enables law enforcement departments to determine which areas, people, and events pose a significant threat to law and order through the analysis of historical data. For instance, hot spot analysis helps law enforcement officers predict where criminal activity is most likely and when it is expected to occur. The information from the analysis, therefore, helps ensure the proper allocation of officers and resources.
Another potential benefit of the technology is that it significantly reduces the risk involved in law enforcement. Police officers all over the world have to deal with risky situations where they do not have any idea of what they are walking into. Predictive policing could potentially provide necessary information to prepare law enforcement officers for different scenarios, thus, facilitating safer operations.
While predictive policing holds potential benefits to law enforcement, it also has several weaknesses. One severe drawback is that the technology is based on historical data. Criminals devise new procedures and activities to cope with law enforcement operations. A study by Thompson suggests that forecasting crime using algorithms and data mining based on historical events is inaccurate, thus a weakness for predictive policing (Thompson, 2016).
Another significant weakness of predictive policing in law enforcement is complexity. Predictive policing involves data-driven technology that eliminates theoretical approaches to predicting criminal activity. The nature of the technology could, therefore, result in too much emphasis on data correlation, which further complicates the results of the analysis. Overall, predictive analysis often makes it more challenging to interpret the available data for forecasting.
Considering the strengths and weaknesses of the technology for law enforcement organizations, it is evident that the technology has great potential in improving law enforcement. However, it is vital to ensure that the weaknesses are minimized. Predictive policing could potentially be enhanced by artificial intelligence that would reduce the focus on data that would result in more casual results. The technology could, therefore, improve the performance of law enforcement agencies.
Benefits to Stakeholders
Implementing predictive policing has numerous benefits to different stakeholders. The government is one of the most significant beneficiaries of the implementation. Governments all over the world spend a lot of revenue on dealing with the consequences of criminal activity. Implementing the technology potentially reduces the occurrence of criminals and terrorist activity, thus reducing overall government spending.
The business community also benefits as a result of the implementation. According to Meijer & Wessels (2019), security is one of the most important aspects to consider during investments. Implementing predictive policing will help improve the safety from an entrepreneurship point of view. Investors have more confidence investing in a region where they know that criminal activity is identified and tracked before it happens. Predictive policing will help create a positive business environment.
Finally, the technology has numerous advantages to the general public. For instance, it helps create better standards of living. Most poor societies are characterized by a high crime rate, which not only limits development but also creates a risk society. Implementing predictive policing creates investment opportunities that create job opportunities, thus, improving the standards of living.
Privacy and Legal Concerns.
While there exist numerous benefits of implementing predictive policing, there are also significant issues associated with its implementations. According to Thompson (2016), most of the criticism against the implementation of the technology is based on the argument that it is unethical from a privacy point of view. Since the technology is data-driven, it requires the analysis of sensitive personal information such as age, contacts, family, social media activity, and in some cases, telephone records. Also, some people argue that predictive policing supports racism and other forms of oppression since it focuses on previous data and criminal activity that, to some, is discriminative. It is, therefore, evident that implementing the technology raises privacy concerns.
From a legal point of view, the credibility of the technology is questionable. According to Ferguson (2016), predictive policing focuses is mainly on the data, and since it is algorithm-based, it lacks the human reasoning capacity to make the right judgment on potential criminal activity. It is, therefore, not suitable to determine the credibility of a forecast. However, a separate school of thought argues that the technology does not replace humans. It is a tool to help the officers make informed decisions concerning forecasting criminal activity (Ferguson, 2016). It is evident that the legal and privacy concerns are inferior to the benefits of predictive policing
Conclusion
Predictive policing poses significant privacy concerns. However, its implementation plays an integral role in forecasting criminal activity and ensuring law and order. The technology involves the analysis of historical data to provide information for forecasting illegal activity. The technology has both positive and negative implications for law enforcement organizations. It is also potentially beneficial to the government, businesses, and the general public. Despite its benefits, the technology poses significant privacy and legal concerns. Overall predictive policing is crucial to law enforcement in the modern age.
References
Ferguson, A. G. (2016). Policing predictive policing. Wash. UL Rev., 94, 1109.
Meijer, A., & Wessels, M. (2019). Predictive policing: Review of benefits and drawbacks. International Journal of Public Administration, 42(12), 1031-1039.
Perry, W. L. (2013). Predictive policing: The role of crime forecasting in law enforcement operations. Rand Corporation.
Thompson, M. (2016). Analyzing the Efficacy of Predictive Policing in Law Enforcement. Available at SSRN 2891544.