How Baltimore Achieved Historically Low Homicides—With Less Police – Emerald Book

When police pull back: Neighborhood‐level effects of de‐policing on violent and property crime, a research note

Abstract Many U.S. cities witnessed both de‐policing and increased crime in 2020, yet whether the former contributed to the latter remains unclear. Indeed, much of what is known about the effects of proactive policing on crime comes from studies that evaluated highly focused interventions atypical of day‐to‐day policing, used cities as the unit of analysis, or could not rule out endogeneity. This study addresses each of these issues, thereby advancing the evidence base concerning the effects of policing on crime. Leveraging two exogenous shocks presented by the onset of the coronavirus 2019 (COVID‐19) pandemic and social unrest after the murder of George Floyd, we evaluated the effects of sudden and sustained reductions in high‐discretion policing on crime at the neighborhood level in Denver, Colorado. Multilevel models accounting for trends in prior police activity, neighborhood structure, seasonality, and population mobility revealed mixed results. On the one hand, large‐scale reductions in stops and drug‐related arrests were associated with significant increases in violent and property crimes, respectively. On the other hand, fewer disorder arrests did not affect crime. These results were not universal across neighborhoods. We discuss the implications of these findings in light of debates concerning the appropriate role of policing in the 21st century.

When Riot Cops Are Not Enough

In When Riot Cops Are Not Enough, sociologist Mike King examines the policing, and broader political repression, of the Occupy Oakland movement. King’s active and daily participation in that movement provides a unique insider perspective to illustrate how the Oakland police and city administrators lost the ability to effectively control the movement.    

Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement

There are widespread concerns about the use of artificial intelligence in law enforcement. Predictive policing and risk assessment are salient examples. Worries include the accuracy of forecasts that guide both activities, the prospect of bias, and an apparent lack of operational transparency. Nearly breathless media coverage of artificial intelligence helps shape the narrative. In this review, we address these issues by first unpacking depictions of artificial intelligence. Its use in predictive policing to forecast crimes in time and space is largely an exercise in spatial statistics that in principle can make policing more effective and more surgical. Its use in criminal justice risk assessment to forecast who will commit crimes is largely an exercise in adaptive, nonparametric regression. It can in principle allow law enforcement agencies to better provide for public safety with the least restrictive means necessary, which can mean far less use of incarceration. None of this is mysterious. Nevertheless, concerns about accuracy, fairness, and transparency are real, and there are tradeoffs between them for which there can be no technical fix. You can't have it all. Solutions will be found through political and legislative processes achieving an acceptable balance between competing priorities.

Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement

There are widespread concerns about the use of artificial intelligence in law enforcement. Predictive policing and risk assessment are salient examples. Worries include the accuracy of forecasts that guide both activities, the prospect of bias, and an apparent lack of operational transparency. Nearly breathless media coverage of artificial intelligence helps shape the narrative. In this review, we address these issues by first unpacking depictions of artificial intelligence. Its use in predictive policing to forecast crimes in time and space is largely an exercise in spatial statistics that in principle can make policing more effective and more surgical. Its use in criminal justice risk assessment to forecast who will commit crimes is largely an exercise in adaptive, nonparametric regression. It can in principle allow law enforcement agencies to better provide for public safety with the least restrictive means necessary, which can mean far less use of incarceration. None of this is mysterious. Nevertheless, concerns about accuracy, fairness, and transparency are real, and there are tradeoffs between them for which there can be no technical fix. You can't have it all. Solutions will be found through political and legislative processes achieving an acceptable balance between competing priorities.

Predictive Policing Explained

Attempts to forecast crime with algorithmic techniques could reinforce existing racial biases in the criminal justice system.

CRIME WAVE: RAIN CITY

A CRIME WAVE setting

News for Black America - NewsOne

Breaking news for and about Black America: politics, original stories, diverse opinions, current events and their impact on Black lives.

Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial

Racial bias in predictive policing algorithms has been the focus of a number of recent news articles, statements of concern by several national organizations (e.g., the ACLU and NAACP), and simulat...

Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial

Racial bias in predictive policing algorithms has been the focus of a number of recent news articles, statements of concern by several national organizations (e.g., the ACLU and NAACP), and simulat...

Predictive Policing and the Politics of Patterns

Abstract Patterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different modes of reasoning about crime, and the way in which they feed back into policing cultures, opaque. Ultimately, this invites a stronger reflection about the political nature of patterns.

Worst of the Worst - 152 Criminal ICE and Border Agents — Ohio Immigrant Alliance

Video recording, final report, data, and other content from the release of OIA’s new report, “The Worst of the Worst: 152 Sexually and Physically Abusive ICE and Border Agents; Implications for U.S. Communities and Actions Needed from Congress.”

Crime As Proxy For Disorder

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Between Threat and Reality: The National Association for the Advancement of Colored People and the Emergence of Armed Self-Defense in Clarksdale and Natchez, Mississippi, 1960-1965

Annelieke Dirks, Between Threat and Reality: The National Association for the Advancement of Colored People and the Emergence of Armed Self-Defense in Clarksdale and Natchez, Mississippi, 1960-1965, Journal for the Study of Radicalism, Vol. 1, No. 1 (Spring 2007), pp. 71-98

Drugs as weapons against us: the CIA's murderous targeting of SDS, Panthers, Hendrix, Lennon, Cobain, Tupac, and other activists

Drugs as Weapons Against Us meticulously details how a …

Predictive Policing and the Politics of Patterns

Abstract. Patterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rea