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Article

Kenneth J. Peak

Since 9/11 and the burgeoning number of mass shootings across the United States (one of the more recent such tragedies, at a Parkland, Florida high school in February 2018, resulted in 17 people being murdered, 17 wounded, and worldwide student protests for gun control), police at all levels and of all jurisdictions have had to train and prepare for security threats and attacks of all types. Certainly, policing on postsecondary campuses is no exception. Recognizing that campuses are no longer wholly safe, violence-free enclaves, higher education administrators have necessarily sought highly trained and equipped campus police agencies to provide a safer environment for their academic communities. Policing on college and university (postsecondary) campuses has a unique history, philosophy, role, and functions. Specifically, from their humble beginnings in the early 1900s through the social and campus unrest of the 1960s and 1970s, and until today, their administration, jurisdiction, authority, methods, legal mandates, technologies, and personnel have had to evolve with the times and with new challenges. In addition, like their local and state counterparts, they have come to embrace community policing and problem solving as well as develop plans for all types of critical incidents, both acts of nature and acts of terrorism. In short, history has shown that these organizations must be prepared for the entire gamut of human and natural disorder.

Article

Fei Yang

Predictive policing, also known as crime forecasting, is a set of high technologies aiding the police in solving past crimes and pre-emptively fighting and preventing future ones. With the right deployment of such technologies, law enforcement agencies can combat and control crime more efficiently with time and resources better employed and allocated. The current practices of predictive policing include the integration of various technologies, ranging from predictive crime maps and surveillance cameras to sophisticated computer software and artificial intelligence. Predictive analytics help the police make predictions about where and when future crime is most likely to happen and who will be the perpetrator and who the potential victim. The underpinning logic behind such predictions is the predictability of criminal behavior and crime patterns based on criminological research and theories such as rational choice and deterrence theories, routine activities theory, and broken windows theory. Currently many jurisdictions in the United States have deployed or have been experimenting with various predictive policing technologies. The most widely adopted applications include CompStat, PredPol, HunchLab, Strategic Subject List (SSL), Beware, Domain Awareness System (DAS), and Palantir. The realization of these predictive policing analytics systems relies heavily on the technological assistance provided by data collection and integration software, facial/vehicle identification and tracking tools, and surveillance technologies that keep tabs on individual activities both in the physical environment and in the digital world. Some examples of these assisting technologies include Automatic License Plate Recognition (ALPR), Next-Generation Identification (NGI) System, the Global Positioning System (GPS), Automatic Vehicle Location (AVL), next-generation police body-worn cameras (BWC) with facial recognition and tracking functions, aerial cameras and unmanned aircraft systems, DeepFace, Persistent Surveillance Systems, Stingrays/D(i)RT-Box/International Mobile Subscriber Identity Catcher, SnapTrends that monitors and analyzes feeds on Twitter, Facebook, Instagram, Picasa, Flickr, and YouTube. This new fashion of using predictive analytics in policing has elicited extensive doubt and criticism since its invention. Whereas scholarly evaluation research shows mixed findings about how effectively predictive policing actually works to help reduce crime, other concerns center around legal and civil rights issues (including privacy protection and the legitimacy of mass surveillance), inequality (stratified surveillance), cost-effectiveness of the technologies, militarization of the police and its implications (such as worsened relationship and weakened trust between the police and the public), and epistemological challenges to understanding crime. To make the best use of the technologies and avoid their pitfalls at the same time, policymakers need to consider the hotly debated controversies raised in the evolution of predictive policing.

Article

Bryce Elling Peterson and Daniel S. Lawrence

Body-worn cameras (BWCs) are small devices that police officers can affix to their person—in a head-, shoulder-, or chest-mounted position—that can audio and video record their interactions with community members. BWCs have received strong support from the public and, in recent years, widespread buy-in from police leadership and officers because of their ability to improve accountability and transparency and enhance the collection of evidence. Implementation guidelines recommend that officers activate their BWCs during each officer–citizen interaction and inform the people they encounter that they are being recorded. Early research on this technology found that officers equipped with body cameras were significantly less likely to engage in force and receive citizen complaints. However, more recent studies with larger samples have had mixed findings about the impact of body cameras on use of force, citizen complaints, and other police activities and behaviors. Numerous legal and ethical considerations are associated with BWCs, including their implications for privacy concerns and public disclosure. However, police officials, policymakers, civil rights groups, and the public must continue to weigh these privacy concerns against the potential for BWCs to enhance police accountability and transparency. Future scholarship should focus on the degree to which BWCs can improve police–community relations and yield valuable evidence for both criminal cases and internal investigations.