The Effect of Race/Ethnicity on Sentencing: Examining Sentence Type, Jail Length, and Prison Length

Jordan, Kareem L.; Freiburger, Tina L.

doi:10.1080/15377938.2014.984045

Cited by 53 publications

(28 citation statements)

References 36 publications

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“…Recent research into the fairness of the court has recognized that race and ethnicity have different effects on the sentencing of men and women. This research recognizes that the experiences of Black, Hispanic, and White men are different than that of Black, Hispanic, and White women (e.g., Doerner & Demuth, 2014; Freiburger & Hilinski, 2013; Jordan & Freiburger, 2015; Steffensmeier et al, 1998). While this research has helped to develop a better understanding of how race and ethnicity differently affect men and women, the results have been inconsistent.…”

mentioning

confidence: 99%

“…Many studies have found that race significantly impacts sentencing decisions, with Black defendants receiving harsher sentences than White defendants (for reviews see Chiricos & Crawford, 1995; Kramer & Steffensmeier, 1993; Mitchell, 2005; Steffensmeier, Ulmer, & Kramer, 1998; Zatz, 2000). Despite some mixed findings, studies that included ethnicity have mostly found that Hispanic defendants were also sentenced more harshly than White defendants (Engen & Gainey, 2000; Holleran & Spohn, 2004; Jordan & Freiburger, 2015; Spohn & Holleran, 2000; Steffensmeier & Demuth, 2000, 2001; Ulmer & Johnson, 2004).…”

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confidence: 99%

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The Joint Effects of Race, Ethnicity, Gender, and Age on the Incarceration and Sentence Length Decisions

Freiburger

Sheeran

2017

Race and Justice

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The current study adds to the literature examining the effects of race, ethnicity, gender, and age on sentencing decisions. The results indicate that Black and male defendants were more likely to be incarcerated in jail as opposed to receiving a probation sentence than White and female defendants. When race, ethnicity, and gender interactions were considered, it appeared that the race effect was driven by Black males’ reduced likelihood to receive probation as opposed to jail. Black females were the least likely to be jailed. Age interactions revealed that being young disadvantaged Black males but advantaged other groups. The decision to incarcerate a defendant in jail versus prison was not significantly influenced by race, ethnicity, or gender. When sentence length was examined, Black males received significantly shorter jail sentences than all groups except Black females. When age was considered, Hispanic defendants 30–39 received longer jail sentences than almost every group.

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confidence: 99%

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confidence: 99%

The Joint Effects of Race, Ethnicity, Gender, and Age on the Incarceration and Sentence Length Decisions

Freiburger

Sheeran

2017

Race and Justice

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“…Hispanic offenders were also less likely to be sentenced to probation rather than jail compared to white offenders, but did not differ in the decision between prison and jail. Compared to black offenders, Hispanic offenders were less likely to receive probation (without jail) but were also less likely to receive a prison sentence (Jordan & Freiburger, 2015).…”

Section: Race/ethnicity and Probation Jail And Prisonmentioning

confidence: 94%

Investigating the Assignment of Probation Conditions: Heterogeneity and the Role of Race and Ethnicity

Kimchi

2018

J Quant Criminol

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The study explores the assignment of probation sentencing packages using a data matching process for 3,031 adult males convicted of a felony in two urban counties. Distinct classes of combinations of probation conditions are identified using latent class analysis, and the influence of race and ethnicity on selection into probation and assignment to these classes is investigated while controlling for other relevant factors. Results indicate legally relevant factors account for much of the racial and ethnic disparity in the initial in/out decision, but not in the assignment of probation conditions. Black probationers are more likely to be assigned to a wider range of combinations of probation conditions, more likely to be assigned to combinations that impose specific restrictions, and more likely to be assigned to classes with longer jail sentences. Findings are discussed as they relate to theoretical perspectives on judicial decision making, discretion in sentencing, and court contexts.

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“…Our first dataset is a sample of bail outcomes collected from several state courts in the US between 1990–2009 [25]. This dataset consists of past criminal records, demographic attributes, and other details of about 86K defendants who were released on bail (Table 2).…”

Section: Experimental Evaluationmentioning

confidence: 99%

Interpretable Decision Sets

Lakkaraju

Bach

Leskovec

2016

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

457

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One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model’s prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems. Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable. Decision sets are sets of independent if-then rules. Because each rule can be applied independently, decision sets are simple, concise, and easily interpretable. We formalize decision set learning through an objective function that simultaneously optimizes accuracy and interpretability of the rules. In particular, our approach learns short, accurate, and non-overlapping rules that cover the whole feature space and pay attention to small but important classes. Moreover, we prove that our objective is a non-monotone submodular function, which we efficiently optimize to find a near-optimal set of rules. Experiments show that interpretable decision sets are as accurate at classification as state-of-the-art machine learning techniques. They are also three times smaller on average than rule-based models learned by other methods. Finally, results of a user study show that people are able to answer multiple-choice questions about the decision boundaries of interpretable decision sets and write descriptions of classes based on them faster and more accurately than with other rule-based models that were designed for interpretability. Overall, our framework provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency.

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The Effect of Race/Ethnicity on Sentencing: Examining Sentence Type, Jail Length, and Prison Length

Cited by 53 publications

References 36 publications

The Joint Effects of Race, Ethnicity, Gender, and Age on the Incarceration and Sentence Length Decisions

The Joint Effects of Race, Ethnicity, Gender, and Age on the Incarceration and Sentence Length Decisions

Investigating the Assignment of Probation Conditions: Heterogeneity and the Role of Race and Ethnicity

Interpretable Decision Sets

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