This research article aims to examine the factors that influence behavioral intentions using the traffic accident claim system in Indonesia. This research uses the Theory of Planned Behavior, the Model of Technology Acceptance, and the Integrated Theory of Technology Acceptance and Use, as well as using the Structural Equation Modeling-Partial Least Squares. The questionnaire was distributed to 580 respondents from three institutions – police, hospitals, and state-owned insurance companies – using quota sampling techniques. The results showed that TPB, TAM, and UTAUT had a significant influence in encouraging behavioral intentions using the traffic accident claim system. Further, the study found that 21st-century digital skills have a moderate role in the impact of acceptance models, planned behavior, and technology use on behavioral intent. Although the study was conducted in Indonesia and is limited to three institutions, it provides strategic guidance for policymakers to improve the use of traffic accident claim systems and reduce Road Traffic Accidents. Further research is recommended to explore from different angles and focus on service providers/operators of traffic accident claim systems to refine the findings of this study. This study contributes to the literature by investigating the factors that influence behavioral intentions using traffic accident claim systems and has a high degree of accuracy of answers from the point of view of traffic safety officers.

The full impact of COVID-19 on policing, crime and disorder is slowly being fully unraveled. However, there remains a number of areas of policing that are yet to be examined in detail. Two of these areas include the impact on the intrinsically linked, volume of police recorded intelligence reports, and the use of stop and search. In this study we examine them symbiotically and frame them in the context of the intelligence led policing model, in particular in an effort to understand how national lockdowns in the United Kingdom affected both proactive policing approaches and the underpinning intelligence cycle. To achieve this, we use data from freedom of information requests regarding the annual levels of recorded police intelligence over a 10-year period for 20 police services. To supplement this, we examine overall national monthly volumes of stop and search activity over a 5-year period. Finally, we then use a case study approach of 3 police services to further explore changes in the conduct of stop and search such as the officer defined ethnicity, grounds for search and disposal outcomes. The findings indicate that both recorded intelligence reports and stop and search increased dramatically during periods of lockdown, despite widespread decreases in crime and social mobility. Changes in proportional impact are identified for White and Black citizens, searches for controlled drugs and the no further action disposal, but these are not consistent across police services. Potential causes and implications are then discussed and again, framed within the context of the impact on the intelligence led policing model and wider policing environment.

The study aimed to evaluate the impact of community policing on maintaining community safety and security by measuring the rate of crime prevention. Specifically, it sought to find out how the city administration of Addis Ababa demonstrated and promoted understanding of the community policing effort, information sharing, and community members' opinions regarding community policing in relation to local security procedures. Law enforcement initiatives to improve quality of life and reduce crime are also included. In order to be successful, community policing requires concrete actions and projects, and given the pervasive mistrust in today's public, both police departments and individual officers must take the lead. The findings demonstrated the influence of community policing, information sharing, and their results on local security practices as well as community policing among community members. According to one suggestion, the police should make sure that they interact positively with the community and that people are aware of their involvement in community policing. This way, people will be happy to provide the police with information that could help prevent crime. The public should be encouraged to maintain good connections with the police, as they can enhance community policing. Because it were obvious that doing so will improve society's peace and security, this study showed the need for additional efforts to further cement relationships of collaboration between the police and the community.

This study addressed algorithmic bias in predictive policing, focusing on the Chicago Police Department's Strategic Subject List (SSL) dataset. We specifically focused on identifying and mitigating age-related biases, a notably underexplored area in prior research. Our research introduced Conditional Score Recalibration as a bias mitigation strategy alongside the well-established Class Balancing technique. Conditional Score Recalibration involved reassessing and adjusting risk scores for individuals initially assigned moderately high-risk scores in the dataset. This recalibration marked such individuals as low risk if they met three conditions, namely: no prior arrests for violent offenses, no previous arrests for narcotic offenses, and having never been involved in shooting incidents. These fairness strategies were implemented on the Random Forest model, and the fairness metrics employed included Equality of Opportunity Difference, Average Odds Difference, and Demographic Parity. The results showed a significant improvement in model fairness, particularly for age biases, without compromising the model's accuracy. These findings challenged the often-assumed trade-off between fairness and accuracy, underscoring the feasibility of achieving fairness without compromising accuracy.

The use of crime mapping has been used by the police to inform deployment of resources for many decades. Such approaches are commonly used to underpin crime control strategies designed to prevent or reduce acquisitive crimes such as domestic burglary. In recent decades there has been a shift away from simple hot spot identification to more complex geospatial mapping methodologies, such as near repeat analysis which was developed through research regarding burglary victimisation. One of these newly emerging methodologies is built upon the ecological, optimal forager theory (OFT). Research using this theory to examine domestic burglary offending intimated potential for positive results in predicting areas at risk of future crime. This led to a number of police services using crime analysis methodologies built upon OFT to underpin their deployment of resources in an effort to prevent or reduce domestic burglary through increased capable guardianship. However, to date, there has been no detailed examination of how the police services implemented such approaches. As such, this study seeks to fill this gap by examining OFT strategies implemented within 5 police services. By interviewing participants directly involved in the programs the study gathers views and perspectives of its relative success. As a result, we identify that participants felt the strategies produced limited impact on recorded burglary crime. We discuss how despite some positive by-products of the strategies, failure to comprehensively apply the theoretical foundations of OFT, and a variety of implementation failures have undermined the various programs, ultimately impacting their effectiveness.

The prognostic abilities of artificial intelligence and neuroscience in forensics and the criminal justice system stand as a reformatory paradigm for understanding any criminal conduct. While the use of artificial intelligence has been labeled transformational data analytical capabilities, neural predictive approaches also enable an intricate understanding of culpability and criminal propensities. The literature on the complex nature of neuroprediction and artificial intelligence, its ethical deliberations and its usability in curving recidivism are analyzed. This theoretical review elucidates the complex interplay, nuptial relationships and convergence of these relationships in the quest for justice. The consequences of not protecting individual rights in the criminal justice system are surveyed using grounded theory. The degree of acceptability and dependability of AI-generated evidence in legal proceedings are also reviewed. All these topics are yet to be contemplated under one roof to offer an argumentative view. The author expects to prompt readers and new commers to embrace more sociolegal and technological research before incorporating such research in the Indian Judiciary. The review focuses on the question of whether to blame such technology inclusion wholly or rather to prioritize the acquisition of bias-free pretrained datasets and processing models.