In today’s world, most actions we take in our everyday activities results in some big corporation collecting our information without our knowledge. This data collection seems like a natural progression in the world of technological advancement but is in fact something known as the surveillance economy. Surveillance economy is where big corporations monitor consumers in the hopes of using our information to make money. The surveillance economy has also brought with in advancements in other areas beyond the consumer market. The surveillance economy has also affected the police officers detect crimes and how their resources are utilized.
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In this paper, I will be discussing the topic of predictive policing and how it affects the general public. I will be discussing what predictive policing is and how it works. I will then, review two concepts which are intertwined in the predictive policing module namely blue and black data and examine how both of those forms of data impact predictive policing. Then, I will discuss the debates surrounding the predictive policing process and analysis the advantages and disadvantages of the system. Lastly , I will discuss Predpol which is a company which uses a machine-learning algorithm to calculate predictions and examine the biases of this system and how that affects members of the public. The purpose of this paper is to show how predictive policing has changed the framework of police work and how those changes can be beneficial but also examining how these changes have negatively affected law enforcement and subsequently the people who law enforcement official serve.
Predictive Policing uses computers to analyze the big data regarding crime in a geographical area in an attempt to predict where and when a crime will take place in the near future (Perry et al, 2013). While this system cannot determine who is more likely to commit a crime, it does locate problematic areas in which law enforcement should focus their resources (Perry et al, 2013). Police officers can use this information to either stop a crime in progress or their presence in that particular area can prevent the crime from being committed in the first place (Perry et al, 2013).
Research has discovered that there is four comprehensive classifications of predictive policing which differs based on the quantity and the intricacy of the data provided (Perry et al, 2013). This methods include, methods of predicting crimes, methods of predicting offenders, methods for calculating perpetrators’ identities and methods for predicting victims of crime (Perry et all, 2013). The methods of predicting crime are the methods uses to predict places and times with an increased probability of crime. The methods of predicting offenders are the tactics which identify individuals at risk of offending in the future (Perry et al, 2013). Methods of predicting perpetrators’ identities are the procedures that are utilize to create profiles that accurately match likely offenders with specific past crimes. Methods of forecasting victims of crimes are approaches used to identify groups or individuals who are likely to become victims of crimes (Perry et al, 2013).
The progression of the predictive policing protocols do not end at the “predictions”, after these “predictions” have been law enforcement need to proceed with interventions based on those predictions to solve crime or diminish criminal activity (Peery et al, 2013). Predictions are produced through the collection and analysis of historical data on crimes and offenders. After these predictions are produced law enforcement needs to take action, the form of action which they engage in can occur in three different ways ( Perry et al, 2013). These interventions can either be generic which consists of increases resources in areas at greater risk, they could also be crime- specific which means they conduct crime specific interventions or they could be problem- specific which entails addressing specific locations and factors driving crime risk (Peery et al, 2013). The last step in the predictive policing module is for law enforcement agencies to assess the immediate effects of the interventions to ensure that there are not unforeseen observable complications (Perry et al, 2013).
Predictive policing is considered to be a form of data- driven law enforcement. Predictive policing is concerned with where and when crime may happen and we know that the system determines this through the use of big data collection. However, in these paper I will examine two forms of data which is feed into the system and how those forms of data can be beneficial or harmful to the overall running of the system. To help define this term, we will be examining an interview which Andrew Guthrie Ferguson took part in where he answer questions about how big data has affected policing and the role of black and blue data. In an interview with Edward Siddons, Ferguson states that black data is data that is affected by three interrelated issues.
He goes on in the interview to expand on this definition by saying that the first issue is the problem of transparency. There are black box systems which consists of secret algorithms which are difficult to understand by yet, they are influencing the way the police conduct their services. The second matter is related to race. The data which is used in these systems are racially encoded and often this data comes from police officers and can be tainted by racial biases. Lastly, Ferguson states that the reason for calling it black is because it is misleading. He goes on to explain that our laws were developed in a small data age and we are now faced with a big data future and we have not yet acquired a full comprehension of how this data can affect the society and our perspective of the facts.
Ferguson was also asked about his ideas about utilizing “bright data” also known as “blue data” as a ways of combatting the problems which arises with the use of black data. He responds by saying that as much as citizens are being monitored we have always created a system by which police officers are observed as well. There vehicles are equip with GPS, there tactics are monitored and someone always knows where they are going and they are watched on body cameras. Ferguson explains that this is what is considered to be blue data and it can be used to hold the police accountable. Ferguson continues by saying that “bright data” is the transparency that good data can provide in determining the most suitable resolution to a given problem. Bright data can suggest solutions other than policing such as a social worker instead of a police officer or cleaning up a neighbourhood in the place of an increased police presence.
The predictive policing model implies that resources will be implemented to the places and at the times they are need most because of its use of historical data to be able to determine where and when law enforcement resources are needed ( Meijer and Wessels, 2019). This ensures that law enforcement is where they need to be at the right time instead of in other locations where no crimes are being committed. Furthermore, predictive policing can identify potential victims or perpetrators through the use of its algorithms. This algorithms can determine the likelihood of criminal activity by known member of criminal organization and can also identify individuals who are more likely to become offenders in the future ( Meijer and Wessels, 2019).
Shapiro (2019) states that predictive policing enhances the control, certainty and accuracy of police work. Shapiro (2019) also cites a Los Angeles Chief of Police who states that the predictions created by the algorithm provides law enforcement with unique opportunities to respond to crimes more efficiently and thereby making communities safer and improving the quality of life of their citizens. Karppi (2018) states that computer and algorithms are better equip to make connections in the data and that if computers are our future then it would seem that predictive policing would be the most effective way to being a part of that new world.
Although predictive policing can potential do all these different things, researchers have raised some concerns about the system. One of the first apprehensions about the system is that law enforcement agencies do not fully understand the algorithm and how the algorithms produces it output ( Meijer and Wessesls, 2019). Law enforcement needs to be able to sufficiently understand what the data is saying if they are to be able to develop appropriate strategies to deal with the data output ( Meijer and Wessels, 2019). This system also has a problem with transparency due in part to the fact that the system is difficult to understand. This lack of transparency can lead to discrimination of particular individuals and groups (Meijer and Wessels,2019). Its particular drawback touches on the black data mentioned previously in this paper. The system has gaps in areas pertaining to one of the most disadvantaged groups of individuals in our society. In some ways these systems ensures that these individuals will continue to be disadvantaged within our society.
Additionally, Shapiro (2019) concludes that predictive policing practices creates an environment where underprivileged neighbourhoods experience over-policing which often leads to officers abusing their power and the individuals of these communities are the ones who suffer. Richardson, Schultz, and Crawford (2019) examined the data used in this program in three cities to determine whether or not the system was using what they referred to as dirty data and if this data would result in bias predictions. Their researcher showed that the predictive policing system are vulnerable to feedback loops which would result in officers being sent to the same neighbourhoods over and over ( Richardson et al, 2019). The officers are sent back to this communities regardless of the crime rate or criminal activity going on in these places and that is due to biases within the data ( Richardson et al, 2019). Richardson et al (2019) states that police data can be bias because police data is a reflection of police practices and that the data can overlook crucial information that ignore certain types of crimes and criminals.
PredPol is a predictive policing company which uses a machine learning algorithm to determine its predictions (PredPol,2018) . These algorithms are frequently updated as new information is entered into the system over time. PredPol focuses on crim type, crime location and the date/time of the crime to create their predictions ( PredPol, 2018). When the system has generated a predictions, those predictions are illustrated using red boxes on a web interface via Google Maps ( PredPol, 2018). This signify the highest risk areas for a particular day and for the shift, regardless of what shift of the day it may be. Law enforcement is encouraged to spend 10% of their shift time in these Predpol boxes (PredPol, 2018). PredPol also builds patrol heat maps that should give commanding officers the ability to see if any areas within your district is being over- patrolled or under-patrolled.
PredPol is already being utilised by several law enforcement agencies all over America. The aim of using this systems in police work is to help reduce human bias and to help reduce the crime rates( Reynolds, 2018). However, it was discovered that PredPol continues to re-establish the very racial bias its claims that it can eliminate. Research shows that the system will continue to send officers to any areas where an arrest has already been made, simply because there was an arrest at that location( Reynolds, 2018). These locations are often the ones with racial minorities and system takes one arrest as an indicator that more crimes will occur in that area (Reynolds, 2018). The system is over estimating crime in an area without taking into consideration that crimes are being reported in that area due to the police presence the system itself has placed there ( Reynolds, 2018).
References
Ferguson, Andrew Guthrie. 2018. Interview by Edward Siddons. Q&A. Andrew Guthrie Ferguson on the Promises and Pitfalls of Data- Driven Policing. https://apolitical.co/solution_article/big-data-can-help-the-public-keep-an-eye-on-the-police/, May 25.
Karppi, Tero. 2018. “ “The Computer Said So”: On the Ethics, Effectiveness, and Cultural Techniques of Predictive Policing. ” Social Media and Society 1-9
Meijer, Albert and Martijn Wessesls. 2019. “ Predictive Policing: Review of Benefits and Drawbacks.” International Journal of Public Administration 42(12): 1031-1039 DOI: 10.1080/01900692.2019.1575664
Perry, Walter L, Brian McInnis, Carter C. Pierre, Susan C. Smith and John S. Hollywood. 2013 “Predictive Policing: The Role of Crimes Forecasting In Law Enforcement Operations.” Rand Corporation https://www.rand.org/pubs/research_briefs/RB9735.html.
Reynolds, Matt. 2018. “ Biased Policing is Made Worse by Errors in Pre-Crime Algorithms.” Retrieved November 7, 2019 ( https://www.newscientist.com/article/mg23631464-300-biased-policing-is-made-worse-by-errors-in-pre-crime-algorithms/)
Richardson, Rashida, Jason M. Schultz and Kate Crawford. 2019. “ Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems and Justice.” New York University Law Review 94(192): 192-233
Shapiro, Aaron. 2019. “ Predictive Policing for Reform? Indeterminacy and Intervention in Big Data Policing.” Surveillance and Society 17(3/4): 456-472 https://ojs.library.queensu.ca/index.php/surveillance-and-society/index | ISSN: 1477-7487
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