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.
How Baltimore Achieved Historically Low Homicides—With Less Police – Emerald Book
◆ Emerald Pages ◆ public safety / urban policy How Baltimore Achieved Historically Low Homicides—With Less Police Emerald Book Publication August 4, 2026 Updated: August 6, 2026 9 min read Baltimore has engineered a 68% drop in homicides since 2019, outpacing the national average—all while operating with 400 fewer officers. Here's how the city debunked
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 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.
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...
[122] Arresting Flexibility: A QJE field experiment on police behavior with about 40 outcome variables
A forthcoming paper in the Quarterly Journal of Economics (QJE), “A Cognitive View of Policing” (htm), reports results from a field experiment showing that teaching police officers to &…
A Cognitive View of Policing
Abstract What causes adverse policing outcomes, such as excessive uses of force and unnecessary arrests? Prevailing explanations focus on problematic officers or deficient regulations and oversight. We introduce an overlooked perspective. We suggest that the cognitive demands inherent in policing can undermine officer decision making. Unless officers are prepared for these demands, they may jump to conclusions too quickly without fully considering alternative ways of seeing a situation. This can lead to adverse policing outcomes. To test this perspective, we created a new training that teaches officers to consider different ways of interpreting the situations they encounter. We evaluated this training using a randomized controlled trial with 2,070 officers from the Chicago Police Department. In a series of lab assessments, we find that treated officers were significantly more likely to consider a wider range of evidence and develop more explanations for subjects’ actions. Critically, we also find that training affected officer performance in the field, leading to reductions in uses of force, discretionary arrests, and arrests of Black civilians. Meanwhile, officer activity levels remained unchanged, and trained officers were less likely to be injured on duty. Our results highlight the value of considering the cognitive aspects of policing and demonstrate the power of using behaviorally informed approaches to improve officer decision making and policing outcomes.
Predictive Policing Explained
Attempts to forecast crime with algorithmic techniques could reinforce existing racial biases in the criminal justice system.
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
Homebound: The Long-Term Rise in Time Spent at Home Among U.S. Adults
The changes in daily life induced by the COVID-19 pandemic brought renewed attention to longstanding concerns about social isolation in the United States. Despite the links between the physical setting for individuals' daily lives and their connections with family, friends, and the various institutions of collective life, trends in where American adults spend their time have been largely overlooked as researchers have focused on how and with whom they spend their time. This article analyzes data from the American Time Use Survey over a timeframe spanning nineteen years and argues that the changes in Americans' daily routines induced by the COVID era should be seen as an acceleration of a longer-term trend: the rise of time spent at home. Results show that from 2003 to 2022, average time spent at home among American adults has risen by one hour and 39 minutes in a typical day. Time at home has risen for every subset of the population and for virtually all activities. Preliminary analysis indicates that time at home is associated with lower levels of happiness and less meaning, suggesting the need for enhanced empirical attention to this major shift in the setting of American life.
Homebound: The Long-Term Rise in Time Spent at Home Among U.S. Adults
The changes in daily life induced by the COVID-19 pandemic brought renewed attention to longstanding concerns about social isolation in the United States. Despite the links between the physical setting for individuals' daily lives and their connections with family, friends, and the various institutions of collective life, trends in where American adults spend their time have been largely overlooked as researchers have focused on how and with whom they spend their time. This article analyzes data from the American Time Use Survey over a timeframe spanning nineteen years and argues that the changes in Americans' daily routines induced by the COVID era should be seen as an acceleration of a longer-term trend: the rise of time spent at home. Results show that from 2003 to 2022, average time spent at home among American adults has risen by one hour and 39 minutes in a typical day. Time at home has risen for every subset of the population and for virtually all activities. Preliminary analysis indicates that time at home is associated with lower levels of happiness and less meaning, suggesting the need for enhanced empirical attention to this major shift in the setting of American life.
Exclusive: FBI’s New Political Pre-Crime Center
Are your views on the list of 'domestic terrorism' indicators?
What to Do if ICE Invades Your Neighborhood
With federal agents storming the streets of American communities, there’s no single right way to approach this dangerous moment. But there are steps you can take to stay safe—and have an impact.
