  Increasingly, the combination of clinical judgment and predictive risk
modelling have been assisting social workers to segregate children at risk of
maltreatment and recommend potential interventions of authorities. A critical
concern among governments and research communities worldwide is that
misinterpretations due to poor modelling techniques will often result in biased
outcomes for people with certain characteristics (e.g., race, socioeconomic
status). In the New Zealand care and protection system, the over-representation
of M\=aori might be incidentally intensified by predictive risk models leading
to possible cycles of bias towards M\=aori, ending disadvantaged or
discriminated against, in decision-making policies. Ensuring these models can
identify the risk as accurately as possible and do not unintentionally add to
an over-representation of M\=aori becomes a crucial matter. In this article we
address this concern with the application of predictive risk modelling in the
New Zealand care and protection system. We study potential factors that might
impact the accuracy and fairness of such statistical models along with possible
approaches for improvement.
