Introduction
The presence and influence of technology have grown since the beginning of the industrial revolution. Specifically, the availability and disruptive impact of technology have been more apparent in the last three decades largely due to advances in information technology and the internet. Transportation, health, communication, and financial services provide examples of industries that have been transformed by technology. Even aspects of governance have not escaped the pervasiveness of these advancements. Policing has benefitted from technological advances that predate the current technological revolution with radio communication technology providing an exemplar. However, the current revolution has forced the above-mentioned industries to modify their models and address their shortcomings. Therefore, it is tempting to speculate about the future impact of technology on policing.
Background
It is not just police districts that have resorted to technology in a bid to address current challenges, other parts of the criminal justice system have likewise resorted to technology. The underlying theme seems to increase resource utilization efficiency. For example, pretrial risk assessments are used to predict future dangerousness while post-trial sentencing predicts possible recidivism. Ferguson (2017) asserts that it is only natural for policing policy to come under the influence of technology. Therefore, the criminal justice system as a whole and policing policy, in particular, have resorted to technology to provide solutions to current and future challenges.
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Similarly, fiscal demands of police districts seem to have outstripped supply. Consequently, cutbacks, layoffs along with mergers and consolidations of entire police departments have become familiar incidents due to a reduction in funding for police operations. Statistically, Newcombe (2014) asserts that public expenditure for policing had by 2006 grown up to 4 times the rate in 1982. Furthermore, between 2011 and 2013, Congress reduced by almost 45% federal support for criminal justice assistance grant programs. The reduction of finances coupled with the growth in expenditure is responsible for compelling the current trajectory of policing policy.
Literature review
As previously stated, healthcare is among the industries that are leveraging technology to address present and future challenges. Specifically, the industry uses epidemiological patterns to reveal environmental toxins underlying health risks. Similarly, predictive policing uses mathematical and analytical techniques to attain law enforcement objectives such as the prediction of offenders, perpetrator and victim identities, and crimes. Bizzack (2012) forecasted that new technologies, demographic changes, evolving values along with the enduring threat of terrorism will affect policing. He went further and asserted that only technology would benefit policing to the extent that made law enforcement efficient and more demanding. According to Perry et al (2013), proactive policing is preferable to reactionary policing when the former is smart and effective. Due to the above-mentioned capabilities, predictive policing methods have gained currency with law enforcement agencies in the US and UK (Dearden, 2017). Therefore, law enforcement policy has borrowed public health’s epidemiological model and quantitative analytical techniques to identify areas of law enforcement agency intervention and crime prevention or use statistical predictions to solve previous crimes.
The methodology for facilitating increased resource utilization leverages technological capacity with law enforcement agents to create smart tactical and strategic objectives. For example, Newcombe (2014) claims that in addition to disbanding its entire force to create a consolidated regional police force, New Jersey’s Camden County Police Department established a real-time tactical operational intelligence center. Said center gathers information from stationary closed-circuit television (CCTV) cameras, automated license plate readers, gunshot location devices, and patrol car movements to create intelligence that is useful to the officers involved in actual interventions. Equally, Ferguson (2017) claims that major American cities have acquired predictive policing software to address property crimes. This is achieved by the inputting of data like crime type and locations into a computer algorithm with the outcome being daily or hourly crime predictions. This predictive information forms the basis of police operations like patrols, crime prevention, and the arrest of criminals. Coats (2018) states that Hartford, Connecticut, has implemented a pre-crime technological system for monitoring its citizens. This system comprises surveillance equipment, analytical software, and drones. An outcome of the increase in technological capacity and sophistication is a reliance on data for the prediction of crime-related events. Therefore, predictive policing systems are essentially actuarial by design to the extent that they perform risk assessment for law enforcement agencies.
Considering that these systems are meant to address concerns over efficient resource utilization, it is essential to point out possible points of efficiency erosion. Undoubtedly, information and algorithms form the basic parts of predictive policing systems. Hence, system efficiency is a function of the quality of information input and fairness of its algorithms (Coats, 2018). Quality information and a fair algorithm increase resource utilization efficiency. Perry et. al (2014) point out that predictive policing technology is premised on private-sector models for predicting consumer behavior. Providing tactical and strategic situational awareness and providing the strategic basis for efficient and effective policing are the primary functions of such systems. Situational awareness along with the anticipation of human behavior is the basis that policing systems utilize to prevent criminal activity in ways that are both proactive and resource efficient. Therefore, predictive policing systems are only effective when they have access to quality information and unbiased algorithm as the basis of their situational awareness provision.
Furthermore, there are tangible metrics for proving the efficiency of any policing strategy. Perry et. al (2013) observe that an effective strategy should reduce crime rates, increase arrest rates for serious offenses, and an observable positive impact on social and justice outcomes. Coats (2018) states that such technology is responsible for the decline of crime rates in Chicago. This technology has the ability to isolate and monitor data from social media. Not only can it reduce law enforcement biases, but it can also improve their targeting. Therefore, predictive policing strategies can enhance policing efficiency.
An analysis of prevailing technological trends reveals that once innovative technology has been unleashed, it rarely retreats. Bizzack (2012) observed that there would a point when police would have access to images of urban areas for forensic reconstruction of crimes and real-time analysis of images for intelligence. He further noted that automation was bound to reconfigure traditional law enforcement tasks. Equally, Newcombe (2014) states that the Camden Police Department’s tactical operational intelligence center has automated surveillance and other data collection functions to boost resource utilization. Similarly, Coats (2018) observes that the Hartford Police Department has invested in additional analytical software, automated surveillance equipment, and a network of drones. Basically, the software operates the surveillance equipment and network of drones in a law enforcement framework. Such analysis will be of foot traffic for establishing patterns and facial recognition through CCTV. The drone network will pursue and retrieve stolen property or track suspects (Coats, 2018). Predictive policing systems exploit automation and other technological capabilities to reduce costs and other drawbacks associated with human labor in an environment of fiscal pressures. Coincidentally, Bizzack (2012) predicted tighter budgets among the emerging trends that will drive the reliance on predictive policing technology. Therefore, pioneering technology like predictive policing, artificial intelligence based surveillance systems and drones once set free and prove effective will set the basis of policing policy in the immediate and intermediate timeframe.
Equally, technological advancements in surveillance, crime detection, and prevention present potential social and ethical challenges. The increased efficiency and precision associated with predictive policing policies erode the civil liberties and privacy rights of law-abiding citizens. The algorithms in these systems rely on crime data using dimensions like history, crime type, and location. Presently, criminal trends illustrate an overrepresentation of minorities. Thus, when officers approach individuals under the expectation that a crime is about to occur, they will likely perceive said individuals as criminal suspects. Likewise, the use of automated surveillance and analytical software erodes the privacy and civil liberties of law-abiding citizens. Currently, there are privacy and civil liberties rights groups that have voiced concern over the lack of transparency over the methodology of these technologies. Therefore, the increased security benefits resulting from predictive policing will come at the expense of potentially negative social and justice outcomes due to the erosion of civil liberties and privacy rights.
Conclusion
Arguably, the influence of technology in policing policy preexists the current wave of technological innovation. It was during the industrial revolution that such policies began to benefit from technology both at the tactical and strategic levels. Several factors have promoted the current fusion of police policy and technology. Fiscal pressures, new demands for service, demographic changes and the emergence of new pieces of technology provide examples of such factors. Predictive policing works on the principle of actuarial predictions and provides a risk management strategy for law enforcement agencies. Furthermore, this technology provides situational awareness and anticipation of human behavior which justifies its association with crime prevention. Surveillance software and automation will continue to change traditional law enforcement roles, especially facial recognition and drones. Notably, the efficiency gains of this policy will come at the expense of the civil liberties and privacy rights of law-abiding citizens.
References
Bizzack, W., J. (2012). Forecasting the future of Policing. Kentucky Law Enforcement . Retrieved from https://docjt.ky.gov/Magazines/Issue%2041/files/assets/downloads/page0006.pdf
Coats, K. (2018 August 14). The Future Of Policing Using Pre-Crime Technology. Forbes . Retrieved from https://www.forbes.com/sites/forbestechcouncil/2018/08/14/the-future-of-policing-using-pre-crime-technology/#750b8c9c64a1
Dearden, L. (2017October 7). How technology is allowing police to predict where and when crime will happen. The Independent . Retrieved from https://www.independent.co.uk/news/uk/home-news/police-big-data-technology-predict-crime-hotspot-mapping-rusi-report-research-minority-report-a7963706.html
Ferguson, G., A. (2017). Policing Predictive Policing. Washington University Law Review , 94(5). Retrieved from https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=6306&context=law_lawreview
Newcombe, T. (2014 September 26). Forecasting the Future for Technology and Policing. Government technology . Retrieved from https://www.govtech.com/public-safety/Forecasting-the-Future-for-Technology-and-Policing.html
Perry, W., McInnis, B., Price, C., Smith, S., & Hollywood, J. (2013). Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations . RAND Corporation. Retrieved from http://www.jstor.org/stable/10.7249/j.ctt4cgdcz .
