Bail or Jail? Judicial Versus Algorithmic Decision-Making in the Pretrial System
80 Pages Posted: 9 Mar 2020
Date Written: February 1, 2020
To date, there are approximately 60 risk assessment tools deployed in the criminal justice system. These tools aim to differentiate between low-, medium-, and high-risk defendants and to increase the likelihood that only those who pose a risk to public safety or are likely to flee are detained. Proponents of actuarial tools claim that these tools meant to eliminate human biases and to rationalize the decision-making process by summarizing all relevant information in a more efficient way than the human brain. Opponents of such tools fear that in the name of science, actuarial tools reinforce human biases, harm defendants’ rights and increase racial disparities in the system. The gap between the two camps has widened in the last few years, and policy makers are torn between the promises of technology to contribute to a more just system, and a growing movement that calls for the abolishment of the use of actuarial risk assessment tools in general, and machine learning-based tools in particular.
This paper examines the role that the technology play in this debate, and whether deploying AI in existing risk assessment tools realizes the fears hyped in the media or improves our criminal justice system? It focuses on the pretrial stage and examines in depth the seven most commonly used tools. Five of these tools are based on traditional regression analysis, and two have a certain machine-learning component. The paper concludes that, classifying pretrial risk assessment tools as AI-based tools creates the impression that sophisticated robots are taking over the courts and pushing judges from their jobs, but this is far from reality. Despite the hype, there are more similarities than differences between tools based on traditional regression analysis and tools based on machine learning. Robots have a long way to go before they can replace judges, and this is not the solution that this paper is arguing for. The long list of policy recommendations discussed in the last chapter, highlight the extensive work that needs to be done to ensure that risk assessment tools are both accurate and fair toward all members of society. These recommendations are beneficial regardless of the technique used; and especial attention is dedicated to assessing how machine learning would impact those recommendations. For example, the paper argues that detailing each one of the factors used in the tools, to include multiple options to choose from (not juts yes or no question), will be useful for both regression analysis and machine learning, but if machine learning is used, the final score could be more personalized and meaningful because of the ability to zoom in on the unique details of the individual case.
Keywords: Criminal Justice, Pretrial, Artificial Intelligence, Risk Assessment, Algorithms
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