Redefining Police Interactions with People Experiencing Mental Health Crises: Models of Response

In many communities, police are the first and only available responders to mental health crises. Dissatisfaction with this arrangement among all stakeholders, concerns about the criminalization of mental illnesses, and recent evidence that at least one in four people killed in encounters with police have a serious mental illness, have all maintained attention to this issue among researchers, policymakers, and practitioners. The scholarship in this area dates back to the 1960s and has examined the nature and characteristics of police interactions with people with mental illnesses and those experiencing mental health crises, police decision making, use of force, and call resolutions. As models of police–mental health collaboration have emerged, the literature describing different models and their implementation and outcomes had grown, as has the literature on police mental health and deescalation training. More recently, researchers have sought to understand the experiences of people with mental needs in these encounters, and the response model preferences of service users and caregivers. While progress has been made in terms of improving the abilities of police officers to respond to mental health crises, a consistent theme across the literature is the lack of adequate mental health resources for people with mental health needs in the community and as options for officers to resolve mental health crises. For the most part, there is a gaping absence of literature exploring race disparities leading up to mental health crises or in police response to them. However, the current Black Lives Matter movement and calls to “defund” police suggest an urgent need to shift responsibility for mental health crisis response away from law enforcement. Government and private nonprofit groups are working to develop frameworks and guidelines for developing capacity in the mental health system to take on the primary responsibility. This work must be done through a race equity lens.

The Criminal Police Officer: Understanding Factors That Predict Police Crime in the United States

Although police decision making and behavior always face intense public scrutiny, officers’ criminal lifestyles have largely been ignored in national debates and discussions. The primary focus of this study was to understand factors that predicted police criminality. To achieve this objective, we analyzed a nationally representative dataset on officers who were arrested from 2005 to 2011 using advanced statistical approaches. Results obtained using multilevel modeling demonstrate the predictive effects of officer and agency characteristics in explaining police criminality. Specifically, findings reveal differences in types of crimes committed by the officers. For example, male officers engage in crimes that are entirely different from those committed by their female counterparts. Likewise, on-duty officers tend to criminally behave differently from their off-duty counterparts. Furthermore, agency-level factors such as type of agency, number of sworn officers, and location of the agency predict police crime. Current findings highlight the importance of policies that would directly address criminality in law enforcement.

Predicting Crime and Other Uses of Neural Networks in Police Decision Making

Neural networks are a machine learning method that excel in solving classification and forecasting problems. They have also been shown to be a useful tool for working with big data oriented environments such as law enforcement. This article reviews and examines existing research on the utilization of neural networks for forecasting crime and other police decision making problem solving. Neural network models to predict specific types of crime using location and time information and to predict a crime’s location when given the crime and time of day are developed to demonstrate the application of neural networks to police decision making. The neural network crime prediction models utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making. The neural network models are able to predict the type of crime being committed 16.4% of the time for 27 different types of crime or 27.1% of the time when similar crimes are grouped into seven categories of crime. The location prediction neural networks are able to predict the zip code location or adjacent location 31.2% of the time.

INDEPENDENT OVERSIGHT, DATA GOVERNANCE AND THE TEMPORAL CONSTRUCTION OF FORENSIC EVIDENCE: THE MAKING OF THE FIRST STATEWIDE RAPE KIT TRACKING PLATFORM

Police management of sexual assault kits (SAKs) has led to systemic disorganization resulting in lost and forgotten forensic evidence. In response, advocates champion 'sexual assault kit tracking platforms' as a pillar of survivor-centered and trauma-informed approaches to rape kit reform at the state level and to create independent oversight over forensic processes. In 2017, Idaho became the first state to implement a statewide tracking platform. The Idaho Sexual Assault Kit Tracking System (IKTS) allows the public to track kits from distribution to collection, and testing at law enforcement facilities. The emergence of tracking platforms raises questions about what governance paradigms, data relations, and discourses these systems enable. I find concerns about "timeliness" and the temporal life of forensic evidence structured the creation, deployment, and maintenance of IKTS. I argue timeliness is a data governance paradigm with multiple and shifting meanings of temporality that comprise various legal, social, and data relationships. I show how the discourse of tardy, slow, and untimely forensic evidence is a mechanism to codify consistent statewide forensic practice and centralize legal decision-making. The legislature's treatment of SAK disorganization as a problem of unmanaged "temporality" assumes a view of evidence processing as merely and neutrally unmechanized. On one hand, this treatment obscures how racialized rape myths shape police decision-making; on the other, IKTS protocols offer some intervention. I argue this should not be read as signs of a racial justice technofix, but as indications of the limits and possibilities of a "technolegal" response to violence.

Artificial fairness? Trust in algorithmic police decision-making

Objectives Test whether (1) people view a policing decision made by an algorithm as more or less trustworthy than when an officer makes the same decision; (2) people who are presented with a specific instance of algorithmic policing have greater or lesser support for the general use of algorithmic policing in general; and (3) people use trust as a heuristic through which to make sense of an unfamiliar technology like algorithmic policing. Methods An online experiment tested whether different decision-making methods, outcomes and scenario types affect judgements about the appropriateness and fairness of decision-making and the general acceptability of police use of this particular technology. Results People see a decision as less fair and less appropriate when an algorithm decides, compared to when an officer decides. Yet, perceptions of fairness and appropriateness were strong predictors of support for police use of algorithms, and being exposed to a successful use of an algorithm was linked, via trust in the decision made, to greater support for police use of algorithms. Conclusions Making decisions solely based on algorithms might damage trust, and the more police rely solely on algorithmic decision-making, the less trusting people may be in decisions. However, mere exposure to the successful use of algorithms seems to enhance the general acceptability of this technology.

Shaping Migrants as Threats: Multilayered Discretion, Criminalization, and Risk Assessment Tools

This article examines Operation Migrant, initiated by the Norwegian police following the so-called migration crises in Europe in 2015. One of its central aims was, by predicting challenges related to increased migration, to improve resource allocation and prevent crime. By drawing on research on risk and threat assessment as a form of power, this article aims to analyze how risk categories are distributed and translated into a multilayered institutional arrangement where migration is policed as a potential crime. The article examines the indicators that the risk assessments are based on and the measures applied and investigates how discretionary practices make immigrants objects for law enforcement and policing. The article contributes to research on migration control in an ordinary police context, where immigration identity checks become part of the crime reduction strategy. Applying the concept of interpretive flexibility (Collins 1981), I will identify the steps in this chain of translation to explore the leap from targeting potentially criminal asylum seekers to targeting broader groups with temporary residency in Norway. The article analyzes the conditions determining how policing, technologies, and migrants are “co-constructed” in a chain of mediation and translation, which reinforces the view of migrants as risky and criminal. The final section discusses how risk and threat analysis is affected by the notion of the “crimmigrant other” (Franko 2020). In Norway, selectively targeting unwanted migrants as criminals has become dominant in police decision-making at a policy level and everyday practices affecting not only third country nationals but also unwanted eastern Europeans.

Police Response to Latina Immigrant Intimate Partner Violence Victims: A Qualitative Analysis

While an increasing body of literature has assessed police response to intimate partner violence (IPV), a dearth of this research has examined police decision-making with formal reports of IPV among Latina immigrants. Using a LatCrit theoretical framework, the current study addressed this substantive gap through the systematic methods of the grounded theory approach to assess a sample of 36 police case files drawn from an agency located in one of the fifth most populous and diverse US cities. Results from the current study identified five themes related to shortcomings in police response to Latina immigrant IPV victims. The themes included cultural coercive control, language barriers, victim participation, case clearance efforts, and service provision. Policy implications and avenues for future research are discussed.

Artificial Fairness? Trust in Algorithmic Police Decision-Making

Objectives: Test whether: (1) people view a policing decision made by an algorithm as more or less trustworthy than when an officer makes the same decision; (2) people who are presented with a specific instance of algorithmic policing have greater or lesser support for the general use of algorithmic policing in general; and (3) people use trust as a heuristic through which to make sense of an unfamiliar technology like algorithmic policing.Methods: An online experiment tested whether different decision-making methods, outcomes and scenario types affect judgements about the appropriateness and fairness of decision-making, and the general acceptability of police use of this particular technology. Results: People see a decision as less fair and less appropriate when an algorithm decides, compared to when an officer decides. Yet perceptions of fairness and appropriateness were strong predictors of support for police use of algorithms, and being exposed to a successful use of an algorithm was linked via trust in the decision made to greater support for police use of algorithms.Conclusions: Making decisions solely based on algorithms might damage trust, and the more police rely solely on algorithmic decision-making, the less trusting people may be in decisions. However, mere exposure to the successful use of algorithms seems to enhance the general acceptability of this technology.