By Edwin H. Sutherland

Impact on Criminology: The book has significantly influenced criminological thought, leading to extended discussions and research on white collar crime.

White Collar Crime Definition: Sutherland argues that white collar crime is a violation of criminal law and must be considered in criminological theories

Social Impact: White collar crimes create distrust and social disorganization, affecting social morale more than ordinary crimes.

Research and References: The document includes numerous references to studies and articles that support the book's findings and arguments.

Yale University Press, 1983, 272 pages

By Vikram Vashisht

This book discusses the impact of corruption on economic development and how it hinders progress.It explores what motivates individuals to engage in corrupt activities for financial gain, and argues that paper currency enables corruption due to its untraceable nature.The author advocates a shift to a digital economy to reduce corruption and boost economic growth.

Taylor & Francis, May 18, 2017, 124 pages

By Verrier Elwin

Anthropological Work: Verrier Elwin's research provides a scientific basis for social work and administration among India's tribal populations, focusing on their distinctive ways of life and culture.

Crime Analysis: The book delves into the psychology and circumstances behind violent crimes and suicides among the Mariatribe, aiming to improve the handling of tribal offenders.

Cultural Practices: It highlights the impact of tribal beliefs, such as witchcraft and magic, on crime and social behavior.

Judicial Challenges: The document discusses the difficulties of applying standard legal practices to tribal areas and the need for a nuanced understanding of tribal mentality.

Indian Branch, 1950, 259 pages

By Deborah Manzi, Francesco Calderoni

This research analyses the resistance and resilience of drug trafficking organizations against law enforcement interventions targeting specific operational roles.

Methods

Using the MADTOR agent-based model, which draws on extensive data from a significant police operation and relevant literature, we simulate the complex dynamics of a major cocaine trafficking and dealing group. The study examined the impact of different arrest scenarios targeting traffickers, packagers, or retailers, on the organization's survival, member count, and revenue.

Results

The findings reveal that interventions targeting traffickers lead to the most significant disruptions, while focusing on retailers also yields substantial impacts. Arresting packagers causes limited disruption.

Conclusions

The findings underscore the importance of role-specific law enforcement approaches in dismantling drug trafficking organizations, considering each role's distinct characteristics and operational importance.

Journal of Criminal Justice, Volume 91, March–April 2024, 102165, 13 pages

By The Violence Policy Center

For the past 25 years, the Violence Policy Center (VPC) has published its annual study When Men Murder Women. Released for Domestic Violence Awareness Month in October, the studies analyzed data from the Federal Bureau of Investigation’s (FBI) Supplementary Homicide Reports (SHR) and ranked the states by their rates of females killed by males in single victim/single offender incidents. In addition to ranking the states by this homicide victimization rate, the studies also offered information on the age and race of these female homicide victims, victim to offender relationship, circumstance, and weapon type. The most recent edition (released in 2022 and which analyzed 2020 SHR data), was the final report to be published by the VPC using SHR data. In January of 2021, the FBI changed the way crime data are collected and reported, which has impacted the reliability of subsequent data. That year, the FBI retired the SHR system and replaced it with the National Incident-Based Reporting System (NIBRS). While NIBRS will eventually provide much more comprehensive and robust crime data compared to the SHR, transitioning law enforcement agencies to the new data collection and reporting system has been slow and burdensome. Indeed, many law enforcement agencies did not transition to NIBRS by January of 2021, which has had a significant impact on the reliability of 2021 crime data. After a careful analysis of that year’s crime data, the VPC has determined that current NIBRS data are not reliable for state-by-state gun violence research as required by When Men Murder Women. 

As a result, for the time being the VPC is unable to continue researching and publishing When Men Murder Women, although we hope that we will be able to resume publication of the report in the future. Though other national data sources contain information about homicides, these data sources do not contain the detailed information that was collected and publicly reported by the SHR (for example, sex of offender, type of firearm, relationship, and circumstance).b Over its 25-year publication history, the findings of the report have: led to the passage of laws that protect women and children from domestic violence, including legislation focused specifically on removing guns from the hands of domestic violence offenders; resulted in statewide public education campaigns; spurred the establishment of domestic homicide review boards; and, been repeatedly cited in the support of legislation and policies that protect women and children, including the federal Violence Against Women Act (VAWA).   

Washington, DC: Violence Policy Center, 2023. 21p.

By Terra Wiens

In 2001, the United States experienced a historic demographic change. For the first time, Hispanics became the largest minority group in the nation, exceeding the number of Black residents.2 With a population in 2020 of 62.1 million, Hispanics represent 18.7 percent of the total population of the United States.3 This study is intended to report on Hispanic homicide victimization and suicide in the United States, the role of firearms in homicide and suicide, and overall gun death figures. Recognizing this demographic landscape, the importance of documenting such victimization is clear. Indeed, studies have found that Hispanic individuals are more likely to die by firearm homicide compared to white, non-Hispanic individuals.  

Washington, DC: Violence Policy Center, 2023. 23p.

By Marty Langley and VPC Executive Director Josh Sugarmann.  

To educate the public and policymakers about the reality of black homicide victimization, each year the VPC releases Black Homicide Victimization in the United States (follow this link to download the study as a pdf). This annual study examines black homicide victimization at the state level utilizing unpublished Supplementary Homicide Report data from the Federal Bureau of Investigation. The study ranks the states by their rates of black homicide victimization and offers additional information for the 10 states with the highest black homicide victimization rates.

Washington, DC: Violence Policy Center, 2023. 18p.

By Stephanie Holmes Didwania

Fraud is one of the most prosecuted crimes in the United States, yet scholarly and journalistic discourse about fraud and other financial crimes tends to focus on the absence of so-called “white-collar” prosecutions against wealthy executives. This Article complicates that familiar narrative. It contains the first nationwide account of how the United States actually prosecutes financial crime. It shows—contrary to dominant academic and public discourse—that the government prosecutes an enormous number of people for financial crimes and that these prosecutions disproportionately involve the least advantaged U.S. residents accused of low-level offenses. This empirical account directly contradicts the aspiration advanced by the FBI and Department of Justice that federal prosecution ought to be reserved for only the most egregious and sophisticated financial crimes. This article argues, in other words, that the term “white-collar crime” is a misnomer.

To build this empirical foundation, the Article uses comprehensive data of the roughly two million federal criminal cases prosecuted over the last three decades matched to county-level population data from the U.S. Census. It demonstrates the history, geography, and inequality that characterize federal financial crime cases, which include myriad crimes such as identity theft, mail and wire fraud, public benefits fraud, and tax fraud, to name just a few. It shows that financial crime defendants are disproportionately low-income and Black, and that this overrepresentation is not only a nationwide pattern, but also a pattern in nearly every federal district in the United States. What’s more, the financial crimes prosecuted against these overrepresented defendants are on average the least serious. This Article ends by exploring how formal law and policy, structural incentives, and individual biases could easily create a prosecutorial regime for financial crime that reinforces inequality based on race, gender, and wealth.

Northwestern Law & Econ Research Paper Forthcoming, outhern California Law Review, Vol. 97, 2024, 54 pages

By Felix Soldner, Bennett Kleinberg, Shane D. Johnson

The popularity of online shopping is steadily increasing. At the same time, fake product reviews are published widely and have the potential to affect consumer purchasing behavior. In response, previous work has developed automated methods utilizing natural language processing approaches to detect fake product reviews. However, studies vary considerably in how well they succeed in detecting deceptive reviews, and the reasons for such differences are unclear. A contributing factor may be the multitude of strategies used to collect data, introducing potential confounds which affect detection performance. Two possible confounds are data-origin (i.e., the dataset is composed of more than one source) and product ownership (i.e., reviews written by individuals who own or do not own the reviewed product). In the present study, we investigate the effect of both confounds for fake review detection. Using an experimental design, we manipulate data-origin, product ownership, review polarity, and veracity. Supervised learning analysis suggests that review veracity (60.26–69.87%) is somewhat detectable but reviews additionally confounded with product-ownership (66.19–74.17%), or with data-origin (84.44–86.94%) are easier to classify. Review veracity is most easily classified if confounded with product-ownership and data-origin combined (87.78–88.12%). These findings are moderated by review polarity. Overall, our findings suggest that detection accuracy may have been overestimated in previous studies, provide possible explanations as to why, and indicate how future studies might be designed to provide less biased estimates of detection accuracy. 

PLoS ONE 17(12): 2022

By Sergi D. Bray , Shane D. Johnson and Bennett Kleinberg

Deepfakes’ are computationally created entities that falsely represent reality. They can take image, video, and audio modalities, and pose a threat to many areas of systems and societies, comprising a topic of interest to various aspects of cybersecurity and cybersafety. In 2020, a workshop consulting AI experts from academia, policing, government, the private sector, and state security agencies ranked deepfakes as the most serious AI threat. These experts noted that since fake material can propagate through many uncontrolled routes, changes in citizen behaviour may be the only effective defence. This study aims to assess human ability to identify image deepfakes of human faces (these being uncurated output from the StyleGAN2 algorithm as trained on the FFHQ dataset) from a pool of non-deepfake images (these being random selection of images from the FFHQ dataset), and to assess the effectiveness of some simple interventions intended to improve detection accuracy. Using an online survey, participants (N = 280) were randomly allocated to one of four groups: a control group, and three assistance interventions. Each participant was shown a sequence of 20 images randomly selected from a pool of 50 deepfake images of human faces and 50 images of real human faces. Participants were asked whether each image was AI-generated or not, to report their confidence, and to describe the reasoning behind each response. Overall detection accuracy was only just above chance and none of the interventions significantly improved this. Of equal concern was the fact that participants’ confidence in their answers was high and unrelated to accuracy. Assessing the results on a per-image basis reveals that participants consistently found certain images easy to label correctly and certain images difficult, but reported similarly high confidence regardless of the image. Thus, although participant accuracy was 62% overall, this accuracy across images ranged quite evenly between 85 and 30%, with an accuracy of below 50% for one in every five images. We interpret the findings as suggesting that there is a need for an urgent call to action to address this threat. 

Journal of Cybersecurity, 2023, 1–18 

By Michael J. Frith  , Kate J. Bowers  , Shane D. Johnson 

Introduction: In response to COVID-19, governments imposed various restrictions on movement and activities. According to the routine activity perspective, these should alter where crime occurs. For burglary, greater household occupancy should increase guardianship against residential burglaries, particularly during the day considering factors such as working from home. Conversely, there should be less eyes on the street to protect against non-residential burglaries. Methods: In this paper, we test these expectations using a spatio-temporal model with crime and Google Community Mobility data. Results: As expected, burglary declined during the pandemic and restrictions. Different types of burglary were, however, affected differently but largely consistent with theoretical expectation. Residential and attempted residential burglaries both decreased significantly. This was particularly the case during the day for completed residential burglaries. Moreover, while changes were coincident with the timing and relaxation of restrictions, they were better explained by fluctuations in household occupancy. However, while there were significant decreases in non-residential and attempted non-residential burglary, these did not appear to be related to changes to activity patterns, but rather the lockdown phase. Conclusions: From a theoretical perspective, the results generally provide further support for routine activity perspective. From a practical perspective, they suggest considerations for anticipating future burglary trends 

Journal of Criminal Justice, v. 82, 2022

By Shane D. Johnson and  Manja Nikolovska

Objectives Routine activity theory suggests that levels of crime are affected by peoples’ activity patterns. Here, we examine if, through their impact on people’s on- and off-line activities, COVID-19 restriction affected fraud committed on- and off-line during the pandemic. Our expectation was that levels of online offending would closely follow changes to mobility and online activity—with crime increasing as restrictions were imposed (and online activity increased) and declining as they were relaxed. For doorstep fraud, which has a different opportunity structure, our expectation was that the reverse would be true. Method COVID-19 restrictions systematically disrupted people’s activity patterns, creating quasi-experimental conditions well-suited to testing the effects of “interventions” on crime. We exploit those conditions using ARIMA time series models and UK data for online shopping fraud, hacking, doorstep fraud, online sales, and mobility to test hypotheses. Doorstep fraud is modelled as a non-equivalent dependent variable, allowing us to test whether findings were selective and in line with theoretical expectations. Results After controlling for other factors, levels of crime committed online were positively associated with monthly variation in online activities and negatively associated with monthly variation in mobility. In contrast, and as expected, monthly variation in doorstep fraud was positively associated with changes in mobility. Conclusions We find evidence consistent with routine activity theory, suggesting that disruptions to people’s daily activity patterns afect levels of crime committed both on- and off-line. The theoretical implications of the findings, and the need to develop a better evidence base about what works to reduce online crime, are discussed. 

Journal of Quantitative Criminology, 2022.