References

Douglas, T., Pugh, Singh, Savulescu, & Fazel. (2017). Risk assessment tools in criminal justice and forensic psychiatry: The need for better data. European Psychiatry, 42, 134-137.

This source provides evidence that data suggest that most risk assessment tools have poor accuracy in most applications and false positives may be especially common in minority ethnic groups. It argues that the use of risk assessment tools is unjustified when it is intended to realize other values, such as justice or public protection, and does not benefit the assessed individual.

 

Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017). Human decisions and machine predictions. Cambridge, MA: National Bureau of Economic Research.

This bibliography specifies details of how a judge decides where defendants will await trial, at home, or in jail. By law, this decision should be based exclusively on a prediction. Authors state that judges use an algorithm to make these predictions and suggest how to improve judicial decisions.

 

Lum, K., & Shah, T. (2019, October 1). Report on measures of fairness in NYC risk assessment tool. HRDAG. Retrieved from https://hrdag.org/2019/09/17/-report-on-measures-of-fairness-nyc/

This report is useful to support my topic because it tries to answer the question of whether a particular risk assessment model reinforces racial inequalities in the criminal justice system. In the report, the authors evaluate the risk assessment tool used in New York City and provide statistics to predict the likelihood that an arrested person will be re-arrested for a felony during the pretrial period.

 

New York City Criminal Justice Agency. Release assessment. (2019, December 12). NYCJA. Retrieved from https://www.nycja.org/-release-assessment

This source is convenient because it shows the release assessment questions. The answer to each question in the assessment predicts an individual’s likelihood to return to court. Knowing the questionnaire used helps determine if the assessment is flawed or discriminatory for blacks and Latinos.

 

Picard, S., Watkins, M., Rempel, M., & Kerodal, A. G. (2019, July 1). Beyond the algorithm: Pretrial reform, risk assessment, and racial fairness. Center for Court Innovation. Retrieved from https://www.courtinnovation.org/-publications/beyond-algorithm

The authors examine whether and how the use of risk assessment tools affects racial disparity in pretrial outcomes. This is helpful because they collected a sample of all arrests made of black, Hispanic, and white individuals in New York City in 2015 and applied their risk assessment tool to this sample of defendants to gain insight into how its use would likely affect the defendant outcomes in New York City.

 

Schwartzapfel, B. (2019, July 1). Can racist algorithms be fixed? The Marshall Project. Retrieved from https://www.themarshallproject.org/2019/07/01/-can-racist-algorithms-be-fixed

Autor states that risk assessments are pitched as race-neutral, replacing human judgment with objective and scientific criteria.  He explains that those black defendants were almost twice as likely as white defendants to be “false positives,” labeled high risk when they did not go on to commit another crime. White defendants who went on to commit another crime, by contrast, were more likely than blacks to be labeled a low risk.