scholarly journals Risk, Actuarialism, and Punishment

Author(s):  
Hazel Kemshall

Risk is a pervasive feature of contemporary life, and has become a key feature of penal policy, systems of punishment, and criminal justice services across a number of the Anglophone jurisdictions. Risk as an approach to calculating the probability of “danger” or “hazard” has its roots in the mercantile trade of the 16th century, growing in significance over the intervening centuries until it pervades both the social and economic spheres of everyday life. Actuarialism, that is the method of statistically calculating and aggregating risk data, has similar roots, steeped in the probability calculations of the insurance industry with 20th-century extension into the arenas of social welfare and penality. Within criminal justice one of the first risk assessment tools was the parole predictor designed by Burgess in 1928. Since then we have seen a burgeoning of risk assessment tools and actuarial risk practices across the penal realm, although the extent to which penality is totally risk based is disputed. Claims for a New Penology centered on risk have been much debated, and empirical evidence would tend toward more cautious claims for such a significant paradigm shift. Prevention and responsibilization are often seen as core themes within risk-focused penality. Risk assessment is used not only to assess and predict future offending of current criminals, but also to enable early identification of future criminals, “high crime” areas, and those in need of early interventions. The ethics, accuracy, and moral justification for such preventive strategies have been extensively debated, with concerns expressed about negative and discriminatory profiling; net-widening; over targeting of minority groups especially for selective incarceration; and more recently criticisms of risk-based pre-emption or “pre-crime” targeting, particularly of ethnic minorities. Responsibilization refers to the techniques of actuarial practices used to make persons responsible for their own risk management, and for their own risk decisions throughout the life course. In respect of offenders this is best expressed through corrective programs focused on “right thinking” and re-moralizing offenders toward more desirable social ends. Those offenders who are “ripe for re-moralization” and who present a level of risk that can be managed within the community can avoid custody or extended sentencing. Those who are not, and who present the highest levels of risk, are justifiably selected for risk-based custodial sentences. Such decision-making not only requires high levels of predictive accuracy, but is also fraught with severe ethical challenges and moral choices, not least about the desired balance between risks, rights, and freedoms.

2017 ◽  
Vol 42 ◽  
pp. 134-137 ◽  
Author(s):  
T. Douglas ◽  
J. Pugh ◽  
I. Singh ◽  
J. Savulescu ◽  
S. Fazel

AbstractViolence risk assessment tools are increasingly used within criminal justice and forensic psychiatry, however there is little relevant, reliable and unbiased data regarding their predictive accuracy. We argue that such data are needed to (i) prevent excessive reliance on risk assessment scores, (ii) allow matching of different risk assessment tools to different contexts of application, (iii) protect against problematic forms of discrimination and stigmatisation, and (iv) ensure that contentious demographic variables are not prematurely removed from risk assessment tools.


Prejudice ◽  
2021 ◽  
pp. 135-154
Author(s):  
Endre Begby

This chapter addresses recent concerns about “algorithmic bias,” specifically in the context of the criminal justice process. Starting from a recent controversy about the use of “automated risk assessment tools” in criminal sentencing and parole hearings, where evidence suggests that such tools effectively discriminate against minority defendants, this chapter argues that the problem here has nothing in particular to do with algorithm-assisted reasoning, nor is it in any clear sense a case of epistemic bias. Rather, given the data set that we are given to work with, there is reason to think that no improvement to our epistemic routines would deliver significantly better results. Instead, the bias is effectively encoded into the data set itself, via a long history of institutionalized racism. This suggests a different diagnosis of the problem: in deeply divided societies, there may just be no way to simultaneously satisfy our moral ideals and our epistemic ideals.


Author(s):  
Dahlnym Yoon ◽  
Daniel Turner ◽  
Verena Klein ◽  
Martin Rettenberger ◽  
Reinhard Eher ◽  
...  

The present study aims at validating the German version of the Structured Assessment of PROtective Factors (SAPROF) for violence risk in a representative sample of incarcerated adult male sexual offenders. Sexual offenders ( n = 450) were rated retrospectively with the SAPROF using the database of the Federal Evaluation Centre for Violent and Sexual Offenders (FECVSO) in the Austrian Prison System. Interrater reliability and predictive validity of the SAPROF scores concerning desistance from recidivism were calculated. Concurrent and incremental validity were tested using the combination of the SAPROF and the Sexual Violence Risk–20 (SVR-20). Interrater reliability was moderate to excellent, and predictive accuracy for various types of recidivism was rather small to moderate. There was a clear negative relationship between the SAPROF and the SVR-20 risk factors. Whereas the SAPROF revealed itself as a significant predictor for various recidivism categories, it did not add any predictive value beyond the SVR-20. Although the SAPROF itself can predict desistance from recidivism, it seems to contribute to the risk assessment in convicted sexual offenders only to a limited extent, once customary risk assessment tools have been applied. Implications for clinical use and further studies are discussed.


CNS Spectrums ◽  
2019 ◽  
Vol 25 (5) ◽  
pp. 593-603
Author(s):  
Sarah L. Desmarais ◽  
Evan M. Lowder

Eligibility criteria for participation in mental health jail diversion programs often specify that, to be diverted, a candidate must not pose a level of threat to public safety that cannot be managed in the community. Risk assessment tools were developed to increase consistency and accuracy in estimates of threat to public safety. Consequently, risk assessment tools are being used in many jurisdictions to inform decisions regarding an individual’s appropriateness and eligibility for mental health jail diversion and the strategies that may be successful in mitigating risk in this context. However, their use is not without controversy. Questions have been raised regarding the validity and equity of their estimates, as well as the impact of their use on criminal justice outcomes. The purpose of this review is to provide an overview of the science and practice of risk assessment to inform decisions and case planning in the context of mental health jail diversion programs. Our specific aims include: (1) to describe the process and components of risk assessment, including differentiating between different approaches to risk assessment, and (2) to consider the use of risk assessment tools in mental health jail diversion programs. We anchor this review in relevant theory and extant research, noting current controversies or debates and areas for future research. Overall, there is strong theoretical justification and empirical evidence from other criminal justice contexts; however, the body of research on the use of risk assessment tools in mental health jail diversion programs, although promising, is relatively nascent.


Author(s):  
THOMAS WHALEN ◽  
GWANGYONG GIM

Modern financial institutions require sophisticated risk assessment tools to integrate human expertise and historical data in a market that is changing and broadening qualitatively, quantitatively, and geographically. The need is especially acute in newly developed countries where expertise and data are scarce, and knowledge bases and assumptions imported from the West may be of limited applicability. Second order logical models can be a valuable tool in such situations. They integrate the robustness of neural or statistical modeling of data, the perspicuity of logical rule induction, and the experience and understanding of skilled human experts. The approach is illustrated in the context of risk assessment in the Korean surety insurance industry.


2016 ◽  
Vol 106 (5) ◽  
pp. 119-123 ◽  
Author(s):  
Sharad Goel ◽  
Justin M. Rao ◽  
Ravi Shroff

In an effort to bring greater efficiency, equity, and transparency to the criminal justice system, statistical risk assessment tools are increasingly used to inform bail, sentencing, and parole decisions. We examine New York City's stop-and-frisk program, and propose two new use cases for personalized risk assessments. First, we show that risk assessment tools can help police officers make considerably better real-time stop decisions. Second, we show that such tools can help audit past actions; in particular, we argue that a sizable fraction of police stops were conducted on the basis of little evidence, in possible violation of constitutional protections.


2011 ◽  
Vol 185 (4S) ◽  
Author(s):  
Samarth Chopra ◽  
David Tamblyn ◽  
Tina Kopsaftis ◽  
Carole Pinnock ◽  
Yu Changhong ◽  
...  

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