Forecasts of Violence to Inform Sentencing Decisions

Objectives

Recent legislation in Pennsylvania mandates that forecasts of "future dangerousness" be provided to judges when sentences are given. Similar requirements already exist in other jurisdictions. Research has shown that machine learning can lead to usefully accurate forecasts of criminal behavior in such setting. But there are settings in which there is insufficient IT infrastructure to support machine learning. The intent of this paper is provide a prototype procedure for making forecasts of future dangerousness that could be used to inform sentencing decisions when machine learning is not practical. We consider how classification trees can be improved so that they may provide an acceptable second choice.

Methods

We apply an version of classifications trees available in R, with some technical enhancements to improve tree stability. Our approach is illustrated with real data that could be used to inform sentencing decisions.

Results

Modest sized trees grown from large samples can forecast well and in a stable fashion, especially if the small fraction of indecisive classifications are found and accounted for in a systematic manner. But machine learning is still to be preferred when practical.

Conclusions

Our enhanced version of classifications trees may well provide a viable alternative to machine learning when machine learning is beyond local IT capabilities.     

Tarasoff in Missouri: The jurisprudence of a mental health provider's duty to warn and protect non-patients of potential risks from patients

In 1976, the Supreme Court of California issued its well-known Tarasoff Principle. From this principle, other courts found a duty to warn, and some found more than just a duty to warn, a duty to protect. As courts in other states adopted a version of the Tarasoff Principle, they issued a wide variety of third-party liability rules. In light of the dynamic, everchanging Tarasoff jurisprudence in the United States and recent relevant appellate court opinion in Missouri, a timely updated summary and update of Tarasoff-related jurisprudence in Missouri is warranted. In the present analysis, we compiled the four appellate court decisions that pertained to the questions of Tarasoff-like third-party liability in the State of Missouri: Sherrill v. Wilson (1983), Matt v. Burrell (1995), Bradley v. Ray (1995), and Virgin v. Hopewell (2001). We reviewed all legal measures for clinicians to protect nonpatients in Missouri, not just those that relate to protecting nonpatients from violence as in a Tarasof-like scenario. Thus, this paper concisely provides a compendium of such options and allows for a meaningful comparison of which legal, protective measures are mandatory and which are permissive, thereby evoking the question of whether measures of protecting nonpatients from a patient's violent acts ought to be mandatory duties or permissive application of professional judgment.     

Regulating technology: Can administrative agencies cope with technological change?

The debate over scientific issues has made a difficult transition from the scientific community to the public-polity process. The traditional view of scientists undertaking “pure” science in their laboratories did not envision government intervention into scientific and technological discovery through administrative agency regulation of risks associated with the results of such research. Yet examples of government regulation of technological risks abound as the nation grapples with nuclear power issues, new-drug testing, and environmental issues, to name a few. This paper considers whether the presently constituted regulatory apparatus is capable of responding to dramatic advances in technology in a timely and effective way. Concluding that it is not, the paper examines the circumscribing characteristics of the regulatory process. Then it discusses alternate approaches for regulating the risks posed by science and technology without doing too much violence to due process or the notion of public participation in the regulatory function.     

Quantifying Hate: The Evolution of German Approaches to Measuring ‘Hate Crime’

This article examines and explains the rise of ‘hate crime’ as a category recognised by the German state. It documents the transition from a fluid and unspecific concern about violence against vulnerable groups in the immediate post-unification years to the formal adoption of ‘hate crime’ and its counterpart ‘right-wing politically motivated crime’ as official statistical categories. It uses theories of policymaking coupled with insights from scholarship on sociological uncertainty and policy transfer to explain the adoption, adaptation, and limitations of the hate crime concept in Germany.