Predictive policing
Predictive policing is a method that employs advanced algorithms and artificial intelligence (AI) to forecast potential criminal activities, allowing law enforcement to allocate resources more efficiently and proactively address crime. This approach can focus on individuals, assessing their likelihood of reoffending based on their past behavior, or target specific geographic areas to anticipate where crimes might occur. The software analyzes vast amounts of data to identify patterns, enabling police departments to prioritize their patrols in areas deemed most at risk for crime.
While proponents argue that predictive policing enhances crime prevention efforts, it has sparked significant controversy. Critics express concerns about the potential for bias in the algorithms, which could lead to the wrongful targeting of innocent individuals. Furthermore, some studies have shown that predictive software may not significantly outperform human judgment, raising questions about its overall effectiveness. As police departments navigate the balance between innovation and community trust, predictive policing remains a contentious topic within discussions of public safety and justice.
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Predictive policing
Predictive policing refers to the use of specialized artificial intelligence or analytical software to predict the likely occurrence of crimes. In some instances, predictive policing is used to predict how likely it is that a previous offender will reoffend. The software then recommends that the police heavily monitor that individual. In other instances, predictive policing software monitors specific areas, then recommends that police increase patrols in regions where higher instances of crimes are likely to be occurring. In order to accomplish these goals, predictive policing software uses complex algorithms to sort through massive amounts of data and establish patterns. It then applies those patterns to events as they unfold, using them to make predictions.
Some people have criticized predictive policing software, arguing that it could become biased. They worry that such a bias could cause innocent people to be arrested. Others criticize the software on its effectiveness record, arguing that, when tested, most software has not outperformed human intuition.
Background
Artificial intelligence (AI) refers to using machines to simulate the intelligence and decision-making capabilities found in biological organisms. In most cases, AI is utilized by computer systems. Though most modern AIs are still primitive compared to the examples given in fiction or the media, scientists are continually making strides towards more complex AI software.
AIs are trained to emulate specific processes more commonly carried out by biological organisms. Most prominent among these processes is learning, meaning the process of acquiring and utilizing new information. Additionally, AI commonly emulates reasoning, or using rules to reach conclusions, and self-correction, or assessing data and fixing inaccuracies.
Computer experts divide AI into four types: reactive machines, limited memory, theory of mind, and self-awareness. The first type, reactive machines, can identify objects and make predictions. It is able to analyze and make decisions in order to achieve a goal. However, it has no memory, and cannot effectively use its past experiences to improve its decision-making process. The second type of AI, limited memory, allows the computer to temporarily store decisions in order to react to them. For example, if a self-driving car observes another vehicle engaging its turn signal, it will retain that information and monitor for the other vehicle to change lanes. However, limited memory AIs do not store data long-term.
The third type of AI, theory of mind, is a program that understands that other beings have beliefs and intentions that impact their decisions. It then accommodates for such variation in its decision-making process. Researchers and scientists are still working on developing such a system. The fourth and final type of AI, self-awareness, involves a machine that is conscious and aware of its own existence. Scientists are still far from developing such a system, and some are concerned about the ethics of working towards its creation.
Overview
Predictive policing refers to the use of complex algorithms and AI tools in order to predict where crimes will occur. This allows police to better utilize their resources, focusing on problem areas and preventing crime instead of reacting to crimes as they happen. Such software is controversial, and it is not in use in every police department. Despite this, many police officers believe that such techniques make them more effective at reducing crime rates.
Different types of predictive policing software produce different types of data for police departments. Some types of predictive policing software, such as Operation LASER, track the information of any registered offender within two years of the present date. It then tracks their various offenses, assigning severity values to each interaction with police. For example, the program would note that a person has been on parole, used to belong to a gang, and has been recently stopped by police. Each of those instances would increase the person's score. If a score hits a certain point value, the program recommends that police forces closely monitor the person by placing them on the Chronic Offender Bulletin. The predictive policing software has independently assessed that such an individual is likely to reoffend in the future. Some models of this type of software independently analyze arrest records to form patterns, giving them even more information to determine the probability than an individual might reoffend in the future.
Other types of predictive policing software monitor locations instead of people, by analyzing incidences of petty crimes and violence in an area, then checking them against established patterns to warn police officers when crime in an area passes a pre-set threshold. Police officers can then devote extra resources to patrolling the area, reducing the crime rate.
Most predictive policing software uses some of six established methodologies to make predictions. Regression methods map statistical correlations between variables that might influence crime rate. Hot-spot analysis predict areas with a high likelihood of crime based on the police department's crime data. Data-mining techniques analyze large quantities of data to establish patterns, then use those patterns to predict future criminal incidents. These techniques commonly utilize complex algorithms to sort through and classify data. Risk-terrain analysis accounts for the various sociological factors that correlate with higher crime in an area. Near-repeat methods track data for specific crimes, then use that data to predict when and where those crimes might occur in the future.
Some critics of predictive policing worry that the software might become biased. They worry that if such an occurrence were to happen, and police or judges put too much stake in the conclusions of the software, it might result in innocent people being convicted. Additionally, current generations of predictive policing software have been criticized for ineffectiveness. In some studies, predictive policing software performed no better at predicting crimes than did human reasoning.
Bibliography
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