Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.
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Within recent years, Financial Credit Risk Assessment (FCRA) has become an increasingly important issue within the financial industry. Therefore, the search for features that can predict the credit risk of an organization has increased. Using multiple statistical techniques, a variance of features has been proposed. Applying a structured literature review, 258 papers have been selected. From the selected papers, 835 features have been identified. The features have been analyzed with respect to the type of feature, the information sources needed and the type of organization that applies the features. Based on the results of the analysis, the features have been plotted in the FCRA Model. The results show that most features focus on hard information from a transactional source, based on official information with a high latency. In this paper, we readdress and -present our earlier work [1]. We extended the previous research with more detailed descriptions of the related literature, findings, and results, which provides a grounded basis from which further research on FCRA can be conducted.
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The HCR-20V3 is a violence risk assessment tool that is widely used in forensic clinical practice for risk management planning. The predictive value of the tool, when used in court for legal decisionmaking, is not yet intensively been studied and questions about legal admissibility may arise. This article aims to provide legal and mental health practitioners with an overview of the strengths and weaknesses of the HCR-20V3 when applied in legal settings. The HCR-20V3 is described and discussed with respect to its psychometric properties for different groups and settings. Issues involving legal admissibility and potential biases when conducting violence risk assessments with the HCR-20V3 are outlined. To explore legal admissibility challenges with respect to the HCR-20V3, we searched case law databases since 2013 from Australia, Canada, Ireland, the Netherlands, New Zealand, the UK, and the USA. In total, we found 546 cases referring to the HCR-20/HCR-20V3. In these cases, the tool was rarely challenged (4.03%), and when challenged, it never resulted in a court decision that the risk assessment was inadmissible. Finally, we provide recommendations for legal practitioners for the cross-examination of risk assessments and recommendations for mental health professionals who conduct risk assessments and report to the court. We conclude with suggestions for future research with the HCR-20V3 to strengthen the evidence base for use of the instrument in legal contexts.
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There is emerging evidence that the performance of risk assessment instruments is weaker when used for clinical decision‐making than for research purposes. For instance, research has found lower agreement between evaluators when the risk assessments are conducted during routine practice. We examined the field interrater reliability of the Short‐Term Assessment of Risk and Treatability: Adolescent Version (START:AV). Clinicians in a Dutch secure youth care facility completed START:AV assessments as part of the treatment routine. Consistent with previous literature, interrater reliability of the items and total scores was lower than previously reported in non‐field studies. Nevertheless, moderate to good interrater reliability was found for final risk judgments on most adverse outcomes. Field studies provide insights into the actual performance of structured risk assessment in real‐world settings, exposing factors that affect reliability. This information is relevant for those who wish to implement structured risk assessment with a level of reliability that is defensible considering the high stakes.
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Editorial on the Research Topic "Leveraging artificial intelligence and open science for toxicological risk assessment"
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Most violence risk assessment tools have been validated predominantly in males. In this multicenter study, the Historical, Clinical, Risk Management–20 (HCR-20), Historical, Clinical, Risk Management–20 Version 3 (HCR-20V3), Female Additional Manual (FAM), Short-Term Assessment of Risk and Treatability (START), Structured Assessment of Protective Factors for violence risk (SAPROF), and Psychopathy Checklist–Revised (PCL-R) were coded on file information of 78 female forensic psychiatric patients discharged between 1993 and 2012 with a mean follow-up period of 11.8 years from one of four Dutch forensic psychiatric hospitals. Notable was the high rate of mortality (17.9%) and readmission to psychiatric settings (11.5%) after discharge. Official reconviction data could be retrieved from the Ministry of Justice and Security for 71 women. Twenty-four women (33.8%) were reconvicted after discharge, including 13 for violent offenses (18.3%). Overall, predictive validity was moderate for all types of recidivism, but low for violence. The START Vulnerability scores, HCR-20V3, and FAM showed the highest predictive accuracy for all recidivism. With respect to violent recidivism, only the START Vulnerability scores and the Clinical scale of the HCR-20V3 demonstrated significant predictive accuracy.
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Risk assessment instruments are widely used to predict risk of adverse outcomes, such as violence or victimization, and to allocate resources for managing these risks among individuals involved in criminal justice and forensic mental health services. For risk assessment instruments to reach their full potential, they must be implemented with fidelity. A lack of information on administration fidelity hinders transparency about the implementation quality, as well as the interpretation of negative or inconclusive findings from predictive validity studies. The present study focuses on adherence, a dimension of fidelity. Adherence denotes the extent to which the risk assessment is completed according to the instrument’s guidelines. We developed an adherence measure, tailored to the ShortTerm Assessment of Risk and Treatability: Adolescent Version (START:AV), an evidence-based risk assessment instrument for adolescents. With the START:AV Adherence Rating Scale, we explored the degree to which 11 key features of the instrument were adhered to in 306 START:AVs forms, completed by 17 different evaluators in a Dutch residential youth care facility over a two-year period. Good to excellent interrater reliability was found for all adherence items. We identified differences in adherence scores on the various START:AV features, as well as significant improvement in adherence for those who attended a START:AV refresher workshop. Outcomes of risk assessment instruments potentially impact decision-making, for example, whether a youth’s secure placement should be extended. Therefore, we recommend fidelity monitoring to ensure the risk assessment practice was delivered as intended.
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Risk assessment plays an important role in forensic mental health care. The way the conclusions of those risk assessments are communicated varies considerably across instruments. In an effort to make them more comparable, Hanson, R. K., Bourgon, G., McGrath, R., Kroner, D. D., Amora, D. A., Thomas, S. S., & Tavarez, L. P. [2017. A five-level risk and needs system: Maximizing assessment results in corrections through the development of a common language. The Council of State Governments Justice Center. https:// csgjusticecenter.org/wp-content/uploads/2017/01/A-Five-Level-Risk-and-Needs-system_Report.pdf] developed the Five-Level Risk and Needs System, placing the conclusions of different instruments along five theoretically meaningful levels. The current study explores a Five-Level Risk and Needs system for violent recidivism to which the numerical codings of the HCR-20 Version 2 and its successor, the HCR-20V3 are calibrated, using a combined sample from six previous studies for the HCR-20 Version 2 (n = 411 males with a violent index offence) and a pilot sample for the HCR-20V3 (n = 66 males with a violent index offence). Baselines for the five levels were defined by a combination of theoretical (e.g. expert meetings) and empirical (e.g. literature review) considerations. The calibration of the HCR-20 Version 2 was able to detect four levels, from a combined level I/II to an adjusted level V. The provisional calibration of the HCR-20V3 showed a substantial overlap with the HCR-20 Version 2, with each level boundary having a 2-point difference. Implications for practice and future research are discussed.
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An effective implementation approach is crucial for successful integration of structured risk assessment instruments into practice. This qualitative study explored barriers and facilitators to the implementation of the Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV) in a Dutch residential youth care service. Perceptions of staff members from various disciplines were gathered through focus group interviews at three consecutive occasions. After inductive coding of the interview extracts using thematic analysis, the identified codes were linked to the consolidated framework for implementation research. Through this framework, factors that influence an implementation project can be organized into multiple domains and constructs. In the present study, staff members described implementation barriers related to characteristics of the risk assessment instrument, staff, and the implementation process. In addition, features of the setting were frequently mentioned as hindering the implementation, such as hierarchy, culture, communication, as well as implementation climate and readiness for change. Staff members also identified multiple facilitators, such as experienced advantages of the START:AV compared to the previous risk assessment practice and positive beliefs about the instrument. The article concludes with recommendations for successful implementation of structured risk assessment instruments in forensic-clinical practice.
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The chapter analyses knowledge management paradigms for the understanding and prioritisation of risks (risk assessment), leading to decision- making amongst policy makers. Studies and approaches on knowledge-based risk assessment, and in general risk management, vary depending on perceptions of risk, and these perceptions affect the knowledge scope and, ultimately, affect decisions on policy. Departing from the problems of big data in aviation, the shortcomings of the existing knowledge management paradigms and the problems of data conversion to knowledge in aviation risk management approaches are discussed. The chapter argues that there is a need for transciplinarity and interdisciplinarity for greater understanding of context, deriving from the challenges in the big data era and in aviation policy making. In order to address the challenging dynamic context in aviation, the chapter proposes a strength/knowledge-based inquiry that involves public sector and high-power organisations, in order to gain holistic knowledge and to aid the decision analysis of policy makers.
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