Law Commons^™

From Negative To Positive Algorithm Rights, Cary Coglianese, Kat Hefter

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Artificial intelligence, or “AI,” is raising alarm bells. Advocates and scholars propose policies to constrain or even prohibit certain AI uses by governmental entities. These efforts to establish a negative right to be free from AI stem from an understandable motivation to protect the public from arbitrary, biased, or unjust applications of algorithms. This movement to enshrine protective rights follows a familiar pattern of suspicion that has accompanied the introduction of other technologies into governmental processes. Sometimes this initial suspicion of a new technology later transforms into widespread acceptance and even a demand for its use. In this paper, we …

After The Crime: Rewarding Offenders’ Positive Post-Offense Conduct, Paul H. Robinson, Muhammad Sarahne

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While an offender’s conduct before and during the crime is the traditional focus of criminal law and sentencing rules, an examination of post-offense conduct can also be important in promoting criminal justice goals. After the crime, different offenders make different choices and have different experiences, and those differences can suggest appropriately different treatment by judges, correctional officials, probation and parole supervisors, and other decision-makers in the criminal justice system.

Positive post-offense conduct ought to be acknowledged and rewarded, not only to encourage it but also as a matter of fair and just treatment. This essay describes four kinds of positive …

Ai In Adjudication And Administration, Cary Coglianese, Lavi M. Ben Dor

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The use of artificial intelligence has expanded rapidly in recent years across many aspects of the economy. For federal, state, and local governments in the United States, interest in artificial intelligence has manifested in the use of a series of digital tools, including the occasional deployment of machine learning, to aid in the performance of a variety of governmental functions. In this paper, we canvas the current uses of such digital tools and machine-learning technologies by the judiciary and administrative agencies in the United States. Although we have yet to see fully automated decision-making find its way into either adjudication …

Bias In, Bias Out, Sandra G. Mayson

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Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic risk assessment to estimate the likelihood that a person will commit future crime. As many scholars have noted, these algorithms tend to have disparate racial impacts. In response, critics advocate three strategies of resistance: (1) the exclusion of input factors that correlate closely with race; (2) adjustments to algorithmic design to equalize predictions across racial lines; and (3) rejection of algorithmic methods altogether.

This Article’s central claim is that these strategies are at best superficial and at worst counterproductive because the source of racial inequality in risk assessment lies …

Pretrial Detention And Bail, Megan Stevenson, Sandra G. Mayson

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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 …