Bail and Pretrial Release

“Poverty Is The New Crime”, 

Michelle Jenkins. The DePaul Journal for Social Justice, 10 DePaul J. Soc. Just. 1 (2017). Accessed June 27, 2017 

Article Introduction: Empirical research shows that pre-trial proceedings in Illinois’ misdemeanor and felony courtrooms have unexpectedly created a divide in access to a fair trial along the lines of income. Therefore, the Illinois pre-trial process should be reformed to eliminate monetized bail bonds. Significant local and national community stakeholders have attempted to increase awareness of systematic issues within the United States criminal justice system. Recurring themes in those stakeholders’ statements include concerns with the over population of American prisons, the economic impact of over incarceration on minority communities, and the unnecessary stressors mandatory sentences place on non-violent misdemeanor offenders. …This article includes a brief historical overview of the pretrial process in Cook County, IL. Additionally, the systematic issues and trends which led to the unexpected divide in access to fair pre-trial treatment along income lines are identified and outlined herein. To solve these issues, I propose bail hearings be reduced to determining whether a defendant should remain in custody or not; I conclude with a recommendation that we phase out the monetary element of bail bonds altogether.

 

“The Downstream Consequences of Misdemeanor Pretrial Detention.” 

Heaton Paul, Mayson Sandra, and Stevenson Megan. (July 2016) University of Baltimore. Accessed June 27, 2017

Paper Abstract: In misdemeanor cases, pretrial detention poses a particular problem because it may induce otherwise innocent defendants to plead guilty in order to exit jail, potentially creating widespread error in case adjudication. While practitioners have long recognized this possibility, empirical evidence on the downstream impacts of pretrial detention on misdemeanor defendants and their cases remains limited. This Article uses detailed data on hundreds of thousands of misdemeanor cases resolved in Harris County, Texas—the third largest county in the U.S.—to measure the effects of pretrial detention on case outcomes and future crime. We find that detained defendants are 25% more likely than similarly situated releasees to plead guilty, 43% more likely to be sentenced to jail, and receive jail sentences that are more than twice as long on average. Furthermore, those detained pretrial are more likely to commit future crime, suggesting that detention may have a criminogenic effect. These differences persist even after fully controlling for the initial bail amount as well as detailed offense, demographic, and criminal history characteristics. Use of more limited sets of controls, as in prior research, overstates the adverse impacts of detention. A quasi-experimental analysis based upon case timing confirms that these differences likely reflect the casual effect of detention. These results raise important constitutional questions, and suggest that Harris County could save millions of dollars a year, increase public safety, and reduce wrongful convictions with better pretrial release policy.

 

Bail Reform: New Directions for Pretrial Detention and Release 

Stevenson, Megan and Mayson, Sandra G. In Academy for Justice, A Report on Scholarship and Criminal Justice Reform (Erik Luna ed., 2017, Forthcoming).; U of Penn Law School, Public Law Research Paper No. 17-18. Accessed June 27, 2017

Abstract: Our current pretrial system imposes high costs on both the people who are detained pretrial and the taxpayers who foot the bill. These costs have prompted a surge of bail reform around the country. Reformers seek to reduce pretrial detention rates, as well as racial and socioeconomic disparities in the pretrial system, while simultaneously improving appearance rates and reducing pretrial crime. The current state of pretrial practice suggests that there is ample room for improvement. Bail hearings are often cursory, with no defense counsel present. Money-bail practices lead to high rates of detention even among misdemeanor defendants and those who pose no serious risk of crime or flight. Infrequent evaluation means that the judges and magistrates who set bail have little information about how their bail-setting practices affect detention, appearance and crime rates. Practical and low-cost interventions, such as court reminder systems, are underutilized. To promote lasting reform, this chapter identifies pretrial strategies that are both within the state’s authority and supported by empirical research. These interventions should be designed with input from stakeholders, and carefully evaluated to ensure that the desired improvements are achieved.

 

“The Effects of Pretrial Detention on Conviction, Future Crime, and Employment: Evidence from Randomly Assigned Judges.”

Dobbie Will, Goldin Jacob, and Yang Crystal. July 2016.


Abstract: Over 20 percent of prison and jail inmates in the United States are currently awaiting trial, but little is known about the impact of pre-trial detention on defendants. This paper uses the detention tendencies of quasi-randomly assigned bail judges to estimate the causal effects of pre-trial detention on subsequent defendant outcomes. Using data from administrative court and tax records, we find that being detained before trial significantly increases the probability of a conviction, primarily through an increase in guilty pleas. Pre-trial detention has no detectable effect on future crime, but decreases pre-trial crime and failures to appear in court. We also find suggestive evidence that pre-trial detention decreases formal sector employment and the receipt of employment- and tax-related government benefits. We argue that these results are consistent with (i) pre-trial detention weakening defendants’ bargaining position during plea negotiations, and (ii) a criminal conviction lowering defendants’ prospects in the formal labor market.

“Pretrial Detention and the Right to be Monitored.”

Wiseman, Samuel. The Yale Law Journal. Vol. 123 No. 5, 1118-1625. March 2014.


Abstract: This Essay develops two related claims. First, in the near term, electronic monitoring will present a superior alternative to money bail for addressing flight risk. In contrast to previous proposals for reducing pretrial detention rates, electronic monitoring has the potential to reduce both fugitive rates (by allowing the defendant to be easily located) and government expenditures (by reducing the number of defendants detained at state expense). Second, despite the potential benefits to defendants and governments, electronic monitoring is not likely to be adopted by legislative or executive action. The best prospect for meaningful change is the Eighth Amendment’s prohibition of excessive bail. To achieve this goal, however, the courts will, for the first time, have to develop a meaningful jurisprudence of excessiveness to test the fit between the government’s pretrial goals and the means employed to accomplish them. This Essay begins this inquiry, arguing that the text, purpose, and history of the Amendment all support the requirement that the chosen means be, at minimum, not substantially more burdensome than necessary. Under this standard, a money bail system that leads to widespread detention without a corresponding increase in performance or savings cannot survive in the face of a less restrictive technological alternative.

Reducing Courts’ Failure to Appear Rate: A procedural Justice Approach.

Bornstein, Brian, H., Tomkins, Alan, J., & Neeley, Elizabeth, M. (May 2011). , Lincoln, NE. University of Nebraska Public Policy Center. 20 United States Courts (July 2013).


Summary: The article examines the usage of written reminders to curtail failure-to-appear (FTA) rates. Various information was gathered including, defendants with higher institutional confidence and those who felt they had been treated more fairly by the criminal justice system were more likely to appear. The study used a control group with no reminder (FTA: 12.6%); then it sent three types of written reminders to defendants: reminder only (FTA: 10.9%), sanctioned reminder (FTA: 8.3%), and positive reminder (FTA: 9.8%).  

An Experiment in Bail Reform. Examining the Impact of the Brooklyn Supervised Release Program

Hahn, Josephine. February 2016.


Abstract: The program launched November 25, 2013. When compared to a matched sample arraigned in the year before program launch, Supervised Release participants were significantly more likely to be released; spent fewer days in detention and were significantly less likely to receive a criminal conviction or jail sentences.

The Bail Trap.

Pinto, Nick. The New York Times. August 13, 2015. Accessed August 18, 2016.


Summary: Every year thousands of innocent people are kept in jail because they can’t afford bail, putting them at risk of losing their jobs, custody of their children, and their lives.

Supervision Costs Significantly Less than Incarceration in Federal System

Uscourts.gov, . Washington, DC. 


Summary: In the federal system in 2013, the average pretrial release costs $7.00, while pretrial detention cost $73.00.

As Court Costs Rise, the Poor are Paying the Price.
Shapiro, Joseph,  NPR. May 19, 2014. Accessed August 22, 2016.


Summary: A Georgia man was kept in jail for six nights after police arrested him for the misdemeanor offense of being a pedestrian under the influence. He was told he could not get out of jail unless he paid the fixed bail amount of $160. "Bail practices that incarcerate indigent individuals before trial solely because of their inability to pay for their release violate the Fourteenth Amendment," the Justice Department said in a friend of court brief, citing the Constitution's guarantee of equal protection.


Link to DOJ’s Amicus curiae brief which can be found by clicking here.

Cash Bail System Damages Defendants, May Harm Public Safety.

University of Pennsylvania Law School’s Quattrone Center for the Fair Administration of Justice. (August 18, 2016). Accessed August 19, 2016.

 

Abstract: Defendants subject to pretrial detention are more likely to be convicted and less likely to receive favorable plea terms than similarly situated defendants who make bail. Moreover, those who experienced pretrial detention committed more crimes after their release than similarly situated individuals who made bail, calling into question the public safety benefits of widespread detention through money bail, particularly in low-level cases.

The money bail system places undue burden on the incarcerated poor- but risk informed release can change that.

USApp - Carmichael, Dottie and Caspers, Heather and Davis, Nicola and Marchbanks, Trey and Naufal, Geroge and Wood, Steve (2017) American Politics and Policy Blog (28 Sep 2017). Accessed March 8, 2018

 

Abstract: The money bail system widely used throughout the American criminal justice system requires a defendant to pay a specified sum of money or await their trail from a jail cell, placing undue burden on the poor. New research by Dottie Carmichael, Heather Caspers, Nicholas Davis, Trey Marchbanks, George Naufal and Steve Wood focuses on the use of validated risk assessment.

The link between bond forfeiture and pretrial release mechanism: The case of Dallas County, Texas.

Stephen J. Clipper, Robert G. Morris, and Amanda Russell-Kaplan. PLOS. August 17, 2017. Accessed March 8, 2018

Abstract: The goal of this study was to evaluate the efficacy of four pretrial jail release mechanisms (i.e., bond types) commonly used during the pretrial phase of the criminal justice process in terms of their ability to discriminate between defendants failing to appear in court (i.e., bond forfeiture). These include attorney bonds, cash bonds, commercial bail bonds, and release via a pretrial services agency.

Bail Reform: New Directions for Pretrial Detention and Release.

Stevenson, Megan T. and Mayson, Sandra G., (March 13, 2017). Academy for Justice, A Report on Scholarship and Criminal Justice Reform (Erik Luna ed., 2017, Forthcoming).; U of Penn Law School, Public Law Research Paper No. 17-18. Accessed March 8, 2018

 

Abstract: Our current pretrial system imposes high costs on both the people who are detained pretrial and the taxpayers who foot the bill. These costs have prompted a surge of bail reform around the country. Reformers seek to reduce pretrial detention rates, as well as racial and socioeconomic disparities in the pretrial system, while simultaneously improving appearance rates and reducing pretrial crime. The current state of pretrial practice suggests that there is ample room for improvement. Bail hearings are often cursory, taking little time to evaluate a defendant’s risks, needs, or ability to pay. Money-bail practices lead to high rates of detention even among misdemeanor defendants and those who pose no serious risk of crime or flight. Infrequent evaluation means that the judges and magistrates who set bail have little information about how their bail-setting practices affect detention, appearance, and crime rates. Practical and low-cost interventions, such as court reminder systems, are underutilized. To promote lasting reform, this chapter identifies pretrial strategies that are both within the state’s authority and supported by empirical research. These interventions should be designed with input from stakeholders, and carefully evaluated to ensure that the desired improvements are achieved.

Human Decisions and Machine Predictions.

Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec,  Jens Ludwig,  Sendhil Mullainathan
The Quarterly Journal of Economics, Volume 133, Issue 1, 1 February 2018, Pages 237–293. Accessed March 8, 2018

 

Abstract: Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.

Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment. Fairness, Accountability and Transparency in Machine Learning.

Zittrain, Jonathan L., Chelsea Barabas, Karthik Dinakar, Joichi Ito, Madars Virza. 2018. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR 81:62-76, 2018. Accessed March 8, 2018

 

Abstract: Actuarial risk assessments might be unduly perceived as a neutral way to counteract implicit bias and increase the fairness of decisions made at almost every juncture of the criminal justice system, from pretrial release to sentencing, parole and probation. In recent times these assessments have come under increased scrutiny, as critics claim that the statistical techniques underlying them might reproduce existing patterns of discrimination and historical biases that are reflected in the data. Much of this debate is centered around competing notions of fairness and predictive accuracy, resting on the contested use of variables that act as “proxies” for characteristics legally protected against discrimination, such as race and gender.  We argue that a core ethical debate surrounding the use of regression in risk assessments is not simply one of bias or accuracy. Rather, it’s one of purpose. If machine learning is operationalized merely in the service of predicting individual future crime, then it becomes difficult to break cycles of criminalization that are driven by the iatrogenic effects of the criminal justice system itself. We posit that machine learning should not be used for prediction, but rather to surface covariates that are fed into a causal model for understanding the social, structural and psychological drivers of crime. We propose an alternative application of machine learning and causal inference away from predicting risk scores to risk mitigation.