scholarly journals Depleted uranium risk assessment for Jefferson Proving Ground using data from environmental monitoring and site characterization. Final report

1996 ◽  
Author(s):  
M.H. Ebinger ◽  
W.R. Hansen
Keyword(s):  
Risk Assessment ◽  
Environmental Monitoring ◽  
Site Characterization ◽  
Depleted Uranium ◽  
Final Report ◽  
Using Data

Environmental monitoring and risk assessment in a tropical Costa Rican catchment under the influence of melon and watermelon crop pesticides

2021 ◽  
pp. 117498
Author(s):  
Carlos E. Rodríguez-Rodríguez ◽  
Jessie Matarrita ◽  
Laia Herrero-Nogareda ◽  
Greivin Pérez-Rojas ◽  
Melvin Alpízar-Marín ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Environmental Monitoring ◽  
Costa Rican

Evidence-Based Sentencing

Criminology ◽  
2021 ◽  
Author(s):  
James C. Oleson
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Risk Measures ◽  
Criminal History ◽  
Community Safety ◽  
Assessment Instruments ◽  
Evidence Based ◽  
Criminogenic Needs ◽  
Actuarial Risk ◽  
Using Data

The evidence-based practice (EBP) movement can be traced to a 1992 article in the Journal of the American Medical Association, although decision-making with empirical evidence (rather than tradition, anecdote, or intuition) is obviously much older. Neverthless, for the last twenty-five years, EBP has played a pivotal role in criminal justice, particularly within community corrections. While the prediction of recidivism in parole or probation decisions has attracted relatively little attention, the use of risk measures by sentencing judges is controversial. This might be because sentencing typically involves both backward-looking decisions, related to the blameworthiness of the crime, as well as forward-looking decisions, about the offender’s prospective risk of recidivism. Evidence-based sentencing quantifies the predictive aspects of decision-making by incorporating an assessment of risk factors (which increase recidivism risk), protective factors (which reduce recidivism risk), criminogenic needs (impairments that, if addressed, will reduce recidivism risk), the measurement of recidivism risk, and the identification of optimal recidivism-reducing sentencing interventions. Proponents for evidence-based sentencing claim that it can allow judges to “sentence smarter” by using data to distinguish high-risk offenders (who might be imprisoned to mitigate their recidivism risk) from low-risk offenders (who might be released into the community with relatively little danger). This, proponents suggest, can reduce unnecessary incarceration, decrease costs, and enhance community safety. Critics, however, note that risk assessment typically looks beyond criminal conduct, incorporating demographic and socioeconomic variables. Even if a risk factor is facially neutral (e.g., criminal history), it might operate as a proxy for a constitutionally protected category (e.g., race). The same objectionable variables are used widely in presentence reports, but their incorporation into an actuarial risk score has greater potential to obfuscate facts and reify underlying disparities. The evidence-based sentencing literature is dynamic and rapidly evolving, but this bibliography identifies sources that might prove useful. It first outlines the theoretical foundations of traditional (non-evidence-based) sentencing, identifying resources and overviews. It then identifies sources related to decision-making and prediction, risk assessment logic, criminogenic needs, and responsivity. The bibliography then describes and defends evidence-based sentencing, and identifies works on sentencing variables and risk assessment instruments. It then relates evidence-based sentencing to big data and identifies data issues. Several works on constitutional problems are listed, the proxies problem is described, and sources on philosophical issues are described. The bibliography concludes with a description of validation research, the politics of evidence-based sentencing, and the identification of several current initiatives.

Layers of Bias: A Unified Approach for Understanding Problems With Risk Assessment

2018 ◽  
Vol 46 (2) ◽  
pp. 185-209 ◽  
Author(s):  
Laurel Eckhouse ◽  
Kristian Lum ◽  
Cynthia Conti-Cook ◽  
Julie Ciccolini
Keyword(s):  
Risk Assessment ◽  
Criminal Justice ◽  
Justice System ◽  
Quantitative Risk Assessment ◽  
Risk Scores ◽  
Risk Models ◽  
Risk Assessment Models ◽  
Final Layer ◽  
The Supreme Court ◽  
Using Data

Scholars in several fields, including quantitative methodologists, legal scholars, and theoretically oriented criminologists, have launched robust debates about the fairness of quantitative risk assessment. As the Supreme Court considers addressing constitutional questions on the issue, we propose a framework for understanding the relationships among these debates: layers of bias. In the top layer, we identify challenges to fairness within the risk-assessment models themselves. We explain types of statistical fairness and the tradeoffs between them. The second layer covers biases embedded in data. Using data from a racially biased criminal justice system can lead to unmeasurable biases in both risk scores and outcome measures. The final layer engages conceptual problems with risk models: Is it fair to make criminal justice decisions about individuals based on groups? We show that each layer depends on the layers below it: Without assurances about the foundational layers, the fairness of the top layers is irrelevant.

scholarly journals Quantitative Microbiological Risk Assessment on Salmonella in Slaughter and Breeder pigs: Final Report

2010 ◽  
Vol 7 (4) ◽  
Author(s):  
Andrew Hill ◽  
Robin Simons ◽  
Vick Ramnial ◽  
Jane Tennant ◽  
Sarah Denman ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Final Report

scholarly journals USING DATA SCIENCE TO RISK ASSESSMENT

2020 ◽  
Author(s):  
Olha Chubukova ◽  
Ihor Ponomarenko ◽  
Oksana Domantovych
Keyword(s):  
Risk Assessment ◽  
Data Science ◽  
Using Data

scholarly journals Quantitative risk assessment of Bacillus cereus in pasteurised milk produced in the Slovak Republic

2014 ◽  
Vol 32 (No. 2) ◽  
pp. 122-131 ◽  
Author(s):  
P. Ačai ◽  
Ľ. Valík ◽  
D. Liptáková
Keyword(s):  
Risk Assessment ◽  
Bacillus Cereus ◽  
Exposure Assessment ◽  
Dose Response ◽  
Slovak Republic ◽  
Reporting Rate ◽  
Assessment Model ◽  
Quantitative Risk Assessment ◽  
Milk Consumption ◽  
Using Data

Quantitative risk assessment of Bacillus cereus using data from pasteurised milk produced in Slovakia was performed. Monte Carlo simulations were used for probability calculation of B. cereus density at the time of pasteurised milk consumption for several different scenarios. The results of the general case exposure assessment indicated that almost 14% of cartons can contain &gt; 10<sup>4</sup> CFU/ml of B. cereus at the time of pasteurised milk consumption. Despite the absence of a generally applicable dose-response relationship that limits a full risk assessment, the probability of intoxication per serving and the estimated number of cases in the population were calculated for the general exposure assessment scenario using an exponential dose-response model based on Slovak data. The mean number of annual cases provided by the risk assessment model for pasteurised milk produced in Slovakia was 0.054/100 000 population. In comparison, the overall reporting rate of the outbreaks in the EU in which B. cereus toxins were the causative agent was 0.02/100 000 population in 2010. Our assessment is in accordance with a generally accepted fact that reporting data for alimentary intoxication are underestimated, mostly due to the short duration of the illness. &nbsp;

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