Another Pandora's box? Some pros and cons of predictive risk modeling

• Algorithms using administrative data can identify children at higher risk of abuse.
• Predictive risk modelling creates opportunities to provide supportive services.
• Early identification of family problems has benefits and limitations.

Early intervention, promoted as being important to the prevention of child maltreatment, is challenged by the difficulty of identifying at risk families before patterns of abuse are established. A way of identifying these families before they reach the radar of statutory systems of child protection is through predictive risk modeling (PRM). Using large datasets PRM tools are able to use algorithms with significant capacity to ascertain and stratify children's risk of experiencing maltreatment in the future. In the process, however, they also identify families who may well benefit from support but are not on a maltreatment trajectory — the so called ‘false positives’ who would not be among those families later identified as mistreating their children. Whilst early identification of families through the use of PRM has the potential to offer opportunities to provide supportive services that could ameliorate future harm to children, it is clear that it also has the potential to mistakenly target and label families as potential child abusers. This article discusses challenges and opportunities associated with the use of PRM in child protection. It briefly discusses the development of PRM in New Zealand, and traverses some of the complex issues as systems attempt to better target limited resources in the context of fiscal restraint.