The cross-cultural fairness of the LS/RNR: An Australian analysis.

Ashford, Linda; Spivak, Benjamin; Ogloff, James R. P.; Shepherd, Stephane M.

doi:10.1037/lhb0000486

Cited by 2 publications

(4 citation statements)

References 40 publications

(82 reference statements)

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“…This result is also unsurprising given most research on risk assessment for FV focuses on men who commit IPV, and there is less research validating risk assessment instruments for women (van der Put et al, 2019). Moreover, there is a lack of research about culturally specific risk factors (Ashford et al, 2022; Mallory et al, 2016), and few instruments have been validated for specific cultural contexts (e.g., Chan, 2012). The poor performance for Māori is especially problematic when considering the ISR’s special commitment to being responsive to Māori communities and including Indigenous service providers (Mossman et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Unpacking Multiagency Structured Professional Judgment Risk Assessments for Family Violence

Simpson

Joshi

Polaschek

2023

J Interpers Violence

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Assessing the risk for future harm is a crucial task for agencies managing Family Violence (FV) cases. The Integrated Safety Response (ISR) is a multiagency collaboration of such agencies operating in two areas of New Zealand, and one of the first steps in their process is to perform a risk assessment. However, in these assessments, it is unclear whether the factors ISR triage team members select are the basis for their overall risk categorization (low, medium, or high), and if those factors are risk factors (i.e., empirical predictors of outcomes). Therefore, in this study we documented the factors ISR triage teams recorded during their risk assessments for 842 FV cases and examined the relationship of those factors with the risk categories. We then investigated whether those factors and the risk categories were indeed capable of predicting FV-related outcomes (recurrence and physical recurrence). We found most of the triage teams’ recorded factors were associated with the risk categories, but fewer than half of the factors were associated with FV-related outcomes. Moreover, the risk categories predicted FV-related outcomes better than chance, but their predictive ability varied across subgroups, performing poorly for aggressors who were Māori or women, and for non-intimate partner cases. We concluded that the ISR triage teams’ risk assessment protocol may benefit from increased structure and validation.

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Section: Discussionmentioning

confidence: 99%

Unpacking Multiagency Structured Professional Judgment Risk Assessments for Family Violence

Simpson

Joshi

Polaschek

2023

J Interpers Violence

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“…In Australia, Aboriginal and Torres Strait Islanders already experience inequality within the criminal justice system, such as significant overincarceration and a decreased likelihood of receiving a diversion (Australian Bureau of Statistics, 2020b; Papalia et al, 2019). Further, a recent study found that Aboriginal and Torres Strait Islanders scored notably higher, and nonreoffenders were more likely to be classified as high risk when compared to non-Aboriginal and Torres Strait Islanders on the LS/RNR (Ashford et al, 2022).…”

Section: Statistical Learning Methods In Risk Assessmentmentioning

confidence: 99%

“…However, these other definitions of fairness have often highlighted unfairness among particular cultural minority groupings (e.g., African Americans and Indigenous populations of North America and Australia) and cultural majority groupings on risk assessment instruments such as the Level of Service instruments (e.g., Level of Service Inventory, Level of Service/Risk–Needs–Responsivity [LS/RNR]). Some cultural minority groupings are often found to score significantly higher (Ashford et al, 2022; Olver et al, 2014), be classified as high risk yet not reoffend more often (i.e., have a higher FPR; Ashford et al, 2022), and are predicted to reoffend more, especially across lower risk scores and risk classifications (Wilson & Gutierrez, 2014). Risk assessment instruments are also often better at predicting reoffending among cultural minority groupings (i.e., a higher PPV), whereas among cultural majority groupings, risk assessment instruments are often better at predicting nonreoffending (i.e., a higher NPV; Shepherd et al, 2015) and are more likely to classify cultural majorities who reoffend as low risk (i.e., a higher FNR; Ashford et al, 2022).…”

Section: Statistical Learning Methods In Risk Assessmentmentioning

confidence: 99%

“…Some cultural minority groupings are often found to score significantly higher (Ashford et al, 2022; Olver et al, 2014), be classified as high risk yet not reoffend more often (i.e., have a higher FPR; Ashford et al, 2022), and are predicted to reoffend more, especially across lower risk scores and risk classifications (Wilson & Gutierrez, 2014). Risk assessment instruments are also often better at predicting reoffending among cultural minority groupings (i.e., a higher PPV), whereas among cultural majority groupings, risk assessment instruments are often better at predicting nonreoffending (i.e., a higher NPV; Shepherd et al, 2015) and are more likely to classify cultural majorities who reoffend as low risk (i.e., a higher FNR; Ashford et al, 2022).…”

Section: Statistical Learning Methods In Risk Assessmentmentioning

confidence: 99%

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Statistical learning methods and cross-cultural fairness: Trade-offs and implications for risk assessment instruments.

Ashford¹,

Spivak²,

Ogloff³

et al. 2023

Psychological Assessment

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The use of statistical learning methods has recently increased within the risk assessment literature. They have primarily been used to increase accuracy and the area under the curve (AUC, i.e., discrimination). Processing approaches applied to statistical learning methods have also emerged to increase cross-cultural fairness. However, these approaches are rarely trialed in the forensic psychology discipline nor have they been trialed as an approach to increase fairness in Australia. The study included 380 Aboriginal and Torres Strait Islander and non-Aboriginal and Torres Strait Islander males assessed with the Level of Service/Risk Needs Responsivity (LS/RNR). Discrimination was assessed through the AUC, and fairness was assessed through the cross area under the curve (xAUC), error rate balance, calibration, predictive parity, and statistical parity. Logistic regression, penalized logistic regression, random forest, stochastic gradient boosting, and support vector machine algorithms using the LS/RNR risk factors were used to compare performance against the LS/RNR total risk score. The algorithms were then subjected to pre- and postprocessing approaches to see if fairness could be improved. Statistical learning methods were found to produce comparable or marginally improved AUC values. Processing approaches increased several fairness definitions (namely xAUC, error rate balance, and statistical parity) between Aboriginal and Torres Strait Islanders and non-Aboriginal and Torres Strait Islanders. The findings demonstrate that statistical learning methods may be a useful approach to increasing the discrimination and cross-cultural fairness of risk assessment instruments. However, both fairness and the use of statistical learning methods encompass significant trade-offs that need to be considered.

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The cross-cultural fairness of the LS/RNR: An Australian analysis.

Cited by 2 publications

References 40 publications

Unpacking Multiagency Structured Professional Judgment Risk Assessments for Family Violence

Unpacking Multiagency Structured Professional Judgment Risk Assessments for Family Violence

Statistical learning methods and cross-cultural fairness: Trade-offs and implications for risk assessment instruments.

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