The combination of clinical judgement and predictive risk models crucially assist social workers to segregate children at risk of maltreatment and decide when authorities should intervene. Predictive risk modelling to address this matter has been initiated by several governmental welfare authorities worldwide involving administrative data and machine learning algorithms. While previous studies have investigated risk factors relating to child maltreatment, several gaps remain as to understanding how such risk factors interact and whether predictive risk models perform differently for children with different features. By integrating Principal Component Analysis and K-Means clustering, this paper presents initial findings of our work on the identification of such features as well as their potential effect on current risk modelling frameworks. This approach allows examining existent, unidentified yet, clusters of New Zealand (NZ) children reported with care and protection concerns, as well as to analyse their inner structure, and evaluate the performance of prediction models trained cluster wise. We aim to discover the extent of clustering degree required as an early step in the development of predictive risk models for child maltreatment and so enhance the accuracy of such models intended for use by child protection authorities. The results from testing LASSO logistic regression models trained on identified clusters revealed no significant difference in their performance. The models, however, performed slightly better for two clusters including younger children. our results suggest that separate models might need to be developed for children of certain age to gain additional control over the error rates and to improve model accuracy. While results are promising, more evidence is needed to draw definitive conclusions, and further investigation is necessary.

The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a human-centered algorithmic design approach, we synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes. We found that most of the literature has focused on risk assessment models but does not consider theoretical approaches (e.g., child-foster parent matching) nor the perspectives of caseworkers (e.g., case notes). Therefore, future algorithms should strive to be context-aware and theoretically robust by incorporating salient factors identified by past research. We provide the HCI community with research avenues for developing human-centered algorithms that redirect attention towards more equitable outcomes for CWS.

It's late November 2016, and I'm squeezed into the far corner of a long row of gray cubicles in the call screening center for the Allegheny County Office of Children, Youth and Families (CYF) child neglect and abuse hotline. We're both studying the Key Information and Demographics System (KIDS), a blue screen filled with case notes, demographic data, and program statistics. We are focused on the records of two families: both are poor, white, and living in the city of Pittsburgh, Pennsylvania. Both were referred to CYF by a mandated reporter, a professional who is legally required to report any suspicion that a child may be at risk of harm from their caregiver. Pat and I are competing to see if we can guess how a new predictive risk model the county is using to forecast child abuse and neglect, called the Allegheny Family Screening Tool (AFST), will score them. According to the US Centers for Disease Control and Prevention, approximately one in four children will experience some form of abuse or neglect in their lifetimes. The agency's Adverse Childhood Experience Study concluded that the experience of abuse or neglect has "tremendous, lifelong impact on our health and the quality of our lives," including increased occurrences of drug and alcohol abuse, suicide attempts, and depression. In the noisy glassed-in room, Pat hands me a double-sided piece of paper called the "Risk/Severity Continuum."