Crime As Proxy For Disorder

CRIME WAVE: RAIN CITY

A CRIME WAVE setting

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.

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.

Clues by Sam

A daily logic puzzle where you deduce who is a criminal!

The Decline of Deviance

Where has all the weirdness gone?

Trouble and Her Friends

Less than a hundred years from now, the forces of law a…

Predictive Policing Explained

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

From Haymarket to Prairieland: How dissent has unleashed the long arm of the law

The Prairieland sentencing has dark historical parallels, Kim Kelly writes. But we can change the end of the current story.

One Nation Under Blackmail: The Sordid Union Between Intelligence and Crime that Gave Rise to Jeffrey Epstein$dVol. 1

Cover Page -- Title Page -- Copyright -- Publisher's Foreword -- Contents -- Introduction -- 1) The Underworld -- 2) Booze and Blackmail -- 3) Organized Crime and the State of Israel -- 4) Roy Cohn's "Favor Bank" -- 5) Shades of Gray -- 6) A Private CIA -- 7) A Killer Enterprise -- 8) Clinton Contra -- 9) High Tech Treason -- 10) Government by Blackmail: The Dark Secrets of the Reagan Era -- Documents -- Index -- List of Abbreviations -- Acknowledgments -- Back Cover

Scamland Myanmar: how conflict and crime syndicates built a global fraud industry - ASPI

While it’s commonly understood that conflict-affected landscapes can often act as safe havens for transnational organised crime, little attention is paid to the central role that state actors play.

Henning Mankell, the master crime writer of Sweden

Author Review

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.

The hacker crackdown: law and disorder on the electronic frontier

A journalist investigates the past, present, and future…