A Decision Tree Approach to Predicting Recidivism in Domestic Violence

Domestic violence (DV) is a global social and public health issue that is highly gendered. Being able to accurately predict DV recidivism, i.e., re-offending of a previously convicted offender, can speed up and improve risk assessment procedures for police and front-line agencies, better protect victims of DV, and potentially prevent future re-occurrences of DV. Previous work in DV recidivism has employed different classification techniques, including decision tree (DT) induction and logistic regression, where the main focus was on achieving high prediction accuracy. As a result, even the diagrams of trained DTs were often too difficult to interpret due to their size and complexity, making decision-making challenging. Given there is often a trade-off between model accuracy and interpretability, in this work our aim is to employ DT induction to obtain both interpretable trees as well as high prediction accuracy. Specifically, we implement and evaluate different approaches to deal with class imbalance as well as feature selection. Compared to previous work in DV recidivism prediction that employed logistic regression, our approach can achieve comparable area under the ROC curve results by using only 3 of 11 available features and generating understandable decision trees that contain only 4 leaf nodes.Comment: 12 pages; Accepted at The 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD

A Decision Tree Approach to Predicting Recidivism in Domestic Violence

Domestic violence (DV) is a global social and public health issue that is highly gendered. Being able to accurately predict DV recidivism, i.e., re-offending of a previously convicted offender, can speed up and improve risk assessment procedures for police and front-line agencies, better protect victims of DV, and potentially prevent future re-occurrences of DV. Previous work in DV recidivism has employed different classification techniques, including decision tree (DT) induction and logistic regression, where the main focus was on achieving high prediction accuracy. As a result, even the diagrams of trained DTs were often too difficult to interpret due to their size and complexity, making decision-making challenging. Given there is often a trade-off between model accuracy and interpretability, in this work our aim is to employ DT induction to obtain both interpretable trees as well as high prediction accuracy. Specifically, we implement and evaluate different approaches to deal with class imbalance as well as feature selection. Compared to previous work in DV recidivism prediction that employed logistic regression, our approach can achieve comparable area under the ROC curve results by using only 3 of 11 available features and generating understandable decision trees that contain only 4 leaf nodes

Serious violent offenders : developing a risk assessment framework

In order to establish a complementary language of risk across all agencies, it is recommended that the Scottish Government and the Risk Management Authority actively disseminate MAPPA guidance through the RMA's specialist training programme and through the development of protocols and memoranda of agreement. Prior to a violent offender framework being implemented, an audit of existing numbers, staffing, budgetary and other resources should be undertaken across the Community Justice Authorities to ascertain projected needs

Measuring Success: An Evaluability Assessment for the Grand Forks Domestic Violence Court

First implemented in the 1990s, specialized domestic violence courts represent one of several solutions developed to improve the response to domestic violence and enhance services for victims (Collins et al., 2021). Other solutions have included mandatory arrest and prosecutorial no-drop policies as well as increased funding support for victim services. There are reportedly over 300 DVCs in the United States as well as 50 in Canada and 100 in the United Kingdom (Eley, 2005; Gutierrez et al., 2016; Hemmens et al., 2020; Home Office, 2008; Tutty & Koshan, 2013). Based on input from a variety of key stakeholders including judges, state’s attorneys, public defense, court administration, and Community Violence Intervention Center (CVIC) staff in 2016, a specialized Domestic Violence Court (DVC) was formally established in Grand Forks (GF) in 2018. It is currently the only DVC court in the state. The GFDVC is a post-conviction specialty court whereby convicted individuals are required to participate in an orientation, intervention programming (such as New Choices facilitated by CVIC), and regular review hearings with Judge Jason McCarthy or Judge Jay Knudson. The goals of the program include increased communication and safety for victims as well as increased compliance and recidivism reduction for the perpetrators. This evaluability assessment briefly summarizes relevant outcome literature pertinent to DVCs, reports the current availability of data maintained by CVIC, and provides short-term and long-term recommendations

Learning Provably Useful Representations, with Applications to Fairness

Representation learning involves transforming data so that it is useful for solving a particular supervised learning problem. The aim is to learn a representation function which maps inputs to some representation space, and an hypothesis which maps the representation space to targets. It is possible to learn a representation function using unlabeled data or data from a probability distribution other than that of the main problem of interest, which is helpful if labeled data is scarce. This approach has been successfully applied in practice, for example through pre-trained neural networks in computer vision and word embeddings in natural language processing. This thesis explores when it is possible to learn representations that are provably useful. We consider learning a representation function from unlabeled data, and propose an approach to identifying conditions where this technique will be useful for a subsequent supervised learning task. The approach requires shared structure in the labeled and unlabeled distributions, as well as a compatible representation function class and hypothesis class. We provide an example where representation learning can exploit cluster structure present in the data. We also consider learning a representation function from a source task distribution and re-using it on a target task of interest, and again propose conditions where this approach will be successful. In this case the conditions depend on shared structure between source and target task distributions. We provide an example involving the transfer of weights in a two-layer feedforward neural network. Representation learning can be applied to another topic of interest: fairness in machine learning. The issue of fairness arises when machine learning systems make or provide advice on decisions about people. A common approach to defining fairness is measuring differences in decisions made by an algorithm for one demographic group compared to another. One approach to preventing discrimination against particular groups is to learn a representation of the data from which it is not possible for an adversary to determine an individual's group membership, but which preserves other useful information. We quantify the costs and benefits of such an approach with respect to several possible fairness definitions. We also examine the relationships between different definitions of fairness and show cases where they cannot simultaneously be satisfied. We explore the use of representation learning for fairness through two case studies: predicting domestic violence recidivism while avoiding discrimination on the basis of race, and predicting student outcomes at university while avoiding discrimination on the basis of gender. Our case studies reveal both the utility of fair representation learning and the trade-offs between accuracy and the definitions of fairness considered

Criminal charges, risk assessment and violent recidivism in cases of domestic abuse

Domestic abuse is a pervasive global problem. Here we analyze two approaches to reducing violent DA recidivism. One involves charging the perpetrator with a crime; the other provides protective services to the victim on the basis of a formal risk assessment carried out by the police. We use detailed administrative data to estimate the average effect of treatment on the treated using inverse propensity-score weighting (IPW). We then make use of causal forests to study heterogeneity in the estimated treatment effects. We find that pressing charges substantially reduces the likelihood of violent recidivism. The analysis also reveals substantial heterogeneity in the effect of pressing charges. In contrast, the risk-assessment process has no discernible effect

Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases

We compare predictions from a conventional protocol-based approach to risk assessment with those based on a machine-learning approach. We first show that the conventional predictions are less accurate than, and have similar rates of negative prediction error as, a simple Bayes classifier that makes use only of the base failure rate. A random forest based on the underlying risk assessment questionnaire does better under the assumption that negative prediction errors are more costly than positive prediction errors. A random forest based on two-year criminal histories does better still. Indeed, adding the protocol-based features to the criminal histories adds almost nothing to the predictive adequacy of the model. We suggest using the predictions based on criminal histories to prioritize incoming calls for service, and devising a more sensitive instrument to distinguish true from false positives that result from this initial screening