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This research examines the direct and interactive effects of defendant race and sex on judicial decisions to utilize mitigating departures in cases involving felony drug convictions in Virginia.

Logistic regression models are used to examine judicial decisions to depart downward in Schedule I & II, and Other (Schedule III, VI, and V), drug cases. The direct and interactive effects of race and sex on departure decisions are modeled separately for Schedule I & II and Other drug offenses.

Defendant race and sex exert both direct and interactive effects on decisions to sentence offenders below the guidelines for both drug categories. Cases involving Black and male defendants, relative to white and female defendants, are significantly less likely to result in mitigating departures for Schedule I & II, and Other drug, violations. The interaction models indicate that cases involving Black male defendants are less likely to result in mitigating departures than other cases, while cases involving white females have higher odds of receiving mitigating departures than other cases.

This chapter adds to the current literature on sentencing disparity by examining unwarranted sentencing disparity in Virginia, where scant research has been conducted. Furthermore, this research models decisions separately by drug category and examines both the direct and interactive effects of race and sex.

This chapter presents four theories that hypothesize race/ethnicity disparities in sentence outcomes. Empirical studies assessing the relationship between defendant’s race/ethnicity and sentence severity are discussed.

I focus on federal sentencing in terms of support or non-support of the theoretical perspectives.

Sentence disparity linked to defendant’s race/ethnicity are observed as net main effects, as a component in joint-conditioning effects with other extralegal defendant characteristics, and as a variable that conditions the effect of process-related mechanism of discretion, and legally relevant case characteristics, and as indirect effects.

Theories share substantial conceptual overlap in specifying the relationship between defendant’s race/ethnicity and predictions of the effect of defendant’s race/ethnicity on sentence severity.

The chapter is based on studies that explored the consideration of motherhood as a mitigating factor when sentencing women to imprisonment in criminal courts in England and Wales. Drawing upon two studies I conducted, it examines the way in which motherhood has been constructed by the courts and the difficulties in centring women's experiences within sentencing. It demonstrates how only by moving to a focus on children's rights has it been possible to ensure sentencers consider the implications of a defendant's motherhood.

The chapter outlines the parameters within which a sentencer can consider motherhood and explores some of the findings from the qualitative research I undertook with Crown Court judges, as part of a larger study in which I also interviewed children whose mother was at the time of interview in prison and the adults providing care to the children in her absence. The studies found that there was inconsistent understanding of a sentencer's duty to consider the impact of any sentence upon dependent children, coupled with a lack of understanding of the consequences of maternal imprisonment for a child. The ways in which this contravenes the Human Rights Act, 1998 and the United Nations Convention on the Rights of the Child 1989 are identified.

The resources which have been developed for sentencing professionals as a consequence of that work are discussed before considering the lack of concern for women's motherhood when imprisoning women on remand or recall. The chapter concludes with a call for motherhood, and not just children's rights, to be centred in sentencing decisions.

The purpose of this study is to examine public perceptions and attitudes toward using artificial intelligence (AI) in the US criminal justice system.

The authors took a quantitative approach and administered an online survey using the Amazon Mechanical Turk platform. The instrument was developed by integrating prior literature to create multiple scales for measuring public perceptions and attitudes.

The findings suggest that despite the various attempts, there are still significant perceptions of sociodemographic bias in the criminal justice system and technology alone cannot alleviate them. However, AI can assist judges in making fairer and more objective decisions by using triangulation – offering additional data points to offset individual biases.

Other scholars can build upon the findings and extend the work to shed more light on some problems of growing concern for society – bias and inequality in criminal sentencing. AI can be a valuable tool to assist judges in the decision-making process by offering diverse viewpoints. Furthermore, the authors bridge the gap between the fields of technology and criminal justice and demonstrate how the two can be successfully integrated for the benefit of society.

To the best of the authors’ knowledge, this is among the first studies to examine a complex societal problem like the introduction of technology in a high-stakes environment – the US criminal justice system. Understanding how AI is perceived by society is necessary to develop more transparent and unbiased algorithms for assisting judges in making fair and equitable sentencing decisions. In addition, the authors developed and validated a new scale that can be used to further examine this novel approach to criminal sentencing in the future.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.

Integrating ontologies with process modeling has gained increasing attention in recent years since it enhances data representations and makes it easier to query, store and reuse knowledge at the semantic level. The authors focused on a process and ontology integration approach by extracting the activities, roles and other concepts related to the process models from organizational sources using natural language processing techniques. As part of this study, a process ontology population (PrOnPo) methodology and tool is developed, which uses natural language parsers for extracting and interpreting the sentences and populating an event-driven process chain ontology in a fully automated or semi-automated (user assisted) manner. The purpose of this paper is to present applications of PrOnPo tool in different domains.

A multiple case study is conducted by selecting five different domains with different types of guidelines. Process ontologies are developed using the PrOnPo tool in a semi-automated and fully automated fashion and manually. The resulting ontologies are compared and evaluated in terms of time-effort and recall-precision metrics.

From five different domains, the results give an average of 70 percent recall and 80 percent precision for fully automated usage of the PrOnPo tool, showing that it is applicable and generalizable. In terms of efficiency, the effort spent for process ontology development is decreased from 250 person-minutes to 57 person-minutes (semi-automated).

The PrOnPo tool is the first one to automatically generate integrated process ontologies and process models from guidelines written in natural language.

This chapter explores some of the risks and constraints associated with defendants’ apologies during allocution at sentencing. It argues that defendants’ stigmatized institutional role identities in conjunction with the constraints imposed by the discursive context of allocution function to limit both the effectiveness with which defendants can speak on their own behalf and the kinds of things that they can say. Allocution has long been understood as a protection for defendants. This chapter proposes that the ideologies associated with this turn at talk have functioned to obscure the ways in which allocution preserves existing power configurations instead of challenging them.

The purpose of this paper is to assess if federal judges have sentenced criminal corporations to fines that are consistent with the seriousness of the offense and the blameworthiness of the organization, which would be in line with the directives from the US Sentencing Guidelines. This paper will also use the focal concerns framework to measure organizational blameworthiness.

This paper uses secondary data from federal sentencing documents, collected by the US Sentencing Commission, for cases that were adjudicated between October 1, 2010 and September 30, 2017.

Results showed that the focal concerns framework can be used to define potential constructs for blameworthiness and that an organization’s culpability score was a significant predictor in whether the company received a higher fine.

The data are unable to examine two of the three measures of focal concerns. Cross-sectional data limits the ability to draw conclusions regarding cause and effect between blameworthiness and monetary fines.

Results imply that judges are sentencing corporations that have higher culpability scores to more severe fines, in accordance with both the federal Sentencing Guidelines and focal concerns framework.

This study is one of the first to apply the focal concerns framework, usually used to examine the sentencing of individuals, to the sentencing of corporations. It is also one of the first to attempt to empirically define blameworthiness.