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Date: April 30, 2024 Tue
Time: 12:51 am
Time: 12:51 am
Results for police data
3 results foundAuthor: Richardson, Rashida Title: Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice Summary: Law enforcement agencies are increasingly using algorithmic predictive policing systems to forecast criminal activity and allocate police resources. Yet in numerous jurisdictions, these systems are built on data produced within the context of flawed, racially fraught and sometimes unlawful practices ('dirty policing'). This can include systemic data manipulation, falsifying police reports, unlawful use of force, planted evidence, and unconstitutional searches. These policing practices shape the environment and the methodology by which data is created, which leads to inaccuracies, skews, and forms of systemic bias embedded in the data ('dirty data'). Predictive policing systems informed by such data cannot escape the legacy of unlawful or biased policing practices that they are built on. Nor do claims by predictive policing vendors that these systems provide greater objectivity, transparency, or accountability hold up. While some systems offer the ability to see the algorithms used and even occasionally access to the data itself, there is no evidence to suggest that vendors independently or adequately assess the impact that unlawful and bias policing practices have on their systems, or otherwise assess how broader societal biases may affect their systems. In our research, we examine the implications of using dirty data with predictive policing, and look at jurisdictions that (1) have utilized predictive policing systems and (2) have done so while under government commission investigations or federal court monitored settlements, consent decrees, or memoranda of agreement stemming from corrupt, racially biased, or otherwise illegal policing practices. In particular, we examine the link between unlawful and biased police practices and the data used to train or implement these systems across thirteen case studies. We highlight three of these: (1) Chicago, an example of where dirty data was ingested directly into the city's predictive system; (2) New Orleans, an example where the extensive evidence of dirty policing practices suggests an extremely high risk that dirty data was or will be used in any predictive policing application, and (3) Maricopa County where despite extensive evidence of dirty policing practices, lack of transparency and public accountability surrounding predictive policing inhibits the public from assessing the risks of dirty data within such systems. The implications of these findings have widespread ramifications for predictive policing writ large. Deploying predictive policing systems in jurisdictions with extensive histories of unlawful police practices presents elevated risks that dirty data will lead to flawed, biased, and unlawful predictions which in turn risk perpetuating additional harm via feedback loops throughout the criminal justice system. Thus, for any jurisdiction where police have been found to engage in such practices, the use of predictive policing in any context must be treated with skepticism and mechanisms for the public to examine and reject such systems are imperative. Details: Unpublished paper, 2019. 30p. Source: Internet Resource: Accessed February 19, 2019 at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3333423 Year: 2019 Country: United States URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3333423 Shelf Number: 154662 Keywords: Civil RightsPolice DataPolice MisconductPolicingPredictive PolicingRacial Bias |
Author: Neusteter, S. Rebecca Title: Every Three Seconds: Unlocking Police Data on Arrests Summary: More than 10 million arrests are made each year in the United States. Although arrest is an important tool in some situations, its overuse can have many detrimental effects. These include, but are not limited to, mass incarceration, diminished public health and economic prosperity, racial inequities, and unwieldy levels of bureaucratic work for officers. The widespread use of arrests also damages already fractured trust between police and many of the communities they serve. Given these impacts, arrests should be monitored carefully and applied sparingly. Alternatives to arrest need to be explored and implemented. However, this space has seen little innovation to date, largely because the data needed to drive and inform change is inaccessible. To help unlock this important knowledge, the Vera Institute of Justice (Vera) developed Arrest Trends. This tool provides answers to fundamental questions about American policing by organizing publicly available datasets into one easy-to-use data platform where users can access, customize, and analyze decades of policing data that previously had been disparately located and difficult to interpret. Users can explore trends in arrests, arrest demographics, clearance rates, victimizations, and data reported at both the local and national levels to understand how these vary by time, location, and offense type. Arrest Trends aims to empower diverse stakeholders such as community advocates, police practitioners, and policymakers to explore and better understand police enforcement. The most important aspects of Arrest Trends are the actionable findings that can be quickly generated and visualized. These findings will create an understanding of, and drive needed improvements to, police enforcement in America. Initial analyses of Arrest Trends' data paint a striking picture, showing that despite recent reductions, the use of arrest is still staggeringly high. The tool reveals that, although arrest volumes have dropped by more than 25 percent since 2006, an arrest is made every three seconds. Fewer than 5 percent of these are for serious violent crimes. Instead, the bulk of police work is in response to incidents that are not criminal in nature and the majority of arrests involve non-serious offenses like "drug abuse violations"-arrests for which increased more than 170 percent between 1980 and 2016-disorderly conduct, and a nondescript low-level offense category known as "all other non-traffic offenses." Collectively, these offenses make up more than 80 percent of all arrests. Further, these heavily arrested non-serious offenses disproportionately impact people of color. The data shows that arrests are applied with geographic disparity as well, concentrating most prominently in metropolitan-and particularly suburban-areas. The enforcement of overwhelmingly low-level offenses may challenge police-community relationships-which are often already frayed-impairing police effectiveness and public safety as a whole. When people do not trust the police, they may be less likely to report crimes or assist in investigations. Indeed, Arrest Trends shows us that fewer than 40 percent of victims report their experiences to the police, and fewer than 25 percent of offenses known to the police are then cleared (meaning that they are solved by arrest). Collectively, the data presented in Arrest Trends, and the findings in this report, challenge the notion that America's reliance on enforcement is a necessary component to achieving oft-stated public safety goals-or indeed, a means of achieving justice or equity. The launch of Arrest Trends marks Vera's most recent effort to reduce the criminal justice system's footprint-by unlocking key policing data and, in doing so, elevating the narrative of over-reliance on arrests and the need for viable alternatives. In this report, readers will find information about the need for greater access to policing data, an overview of the Arrest Trends tool as well as several initial findings gleaned from it, and future directions for this work. Details: New York: Vera Institute of Justice, 2019. 16p. Source: Internet Resource: Accessed February 22, 2019 at: https://www.vera.org/publication_downloads/arrest-trends-every-three-seconds-landing/arrest-trends-every-three-seconds.pdf Year: 2019 Country: United States URL: https://www.vera.org/publication_downloads/arrest-trends-every-three-seconds-landing/arrest-trends-every-three-seconds.pdf Shelf Number: 154721 Keywords: Arrests and Apprehensions Police DataPolice Records |
Author: Bryant, Robin Title: Evaluation of the MPS Predictive Policing Trial Summary: 1 Introduction This report is an evaluation of a trial of predictive policing by the MPS in the Greater London Area. Since 2013, MPS Territorial Policing, Capability and Support (TP C & S) has been trialling a number of predictive policing initiatives in the capital. By late 2014, three commercial predictive policing products and one 'in house' (MPS analyst) crime forecasting model ('MBR') were being used to support operational decision-making in most London boroughs. The commercial products were each allocated two Borough Operational Command Units (BOCU) in which to operate. The MBR (in principle at least) operated in the remaining 26 of the 32 MPS BOCUs. The methods used to conduct this evaluation were as follows. 1.1 Literature review A review of publications, research, media coverage relating to 'predictive policing', crime forecasting and measures of predictive accuracy. 1.2 Deskbound research Analysis of other research conducted into predictive policing, summarising evaluations, analysing the spatial and temporal distribution of crime in a number of localities, analysing dosage rates, comparisons with Kent Police evaluations (unpublished), the Police Service of the Netherlands (unpublished) and analysing stakeholder survey results collected by University of Warwick MSc postgraduate students and by the MPS. 1.3 Assessment of predictive accuracy As part of the evaluation we assessed the hit rates and predictive accuracy indices of the crime forecasting products in two towns outside London: Reading and Slough. This involved the writing of Matlab code to identify the number of successful forecasts, the use of ArcGIS software to analyse the spatial and temporal distribution of crime in Reading and Slough and the analysis of crime data provided by Thames Valley Police. 1.4 Warwick MSc research We have summarised the findings of six University of Warwick MSc Business Analytics and Consulting dissertations and a single MSc dissertation undertaken by a University College London (UCL) researcher during 2014. All of the MSc dissertations discussed, inter alia, five aspects of predictive policing in the MPS: theoretical background, predictive accuracy, operational implementation, 'patrol dosage and user opinion. Details: Canterbury, UK: School of Law, Criminal Justice & Computing, Canterbury Christ Church University, 2015. 52p. Source: Internet Resource: Accessed March 27, 2019 at: https://create.canterbury.ac.uk/15974/1/15974.pdf Year: 2015 Country: United Kingdom URL: https://create.canterbury.ac.uk/15974/1/15974.pdf Shelf Number: 155188 Keywords: Crime Analysis Crime Forecasting Crime Hot Spots Hot Spots Policing Police DataPolice Patrol Predictive Policing |