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Increasingly, the combination of clinical judgment and predictive riskmodelling have been assisting social workers to segregate children at risk ofmaltreatment and recommend potential interventions of authorities. A criticalconcern among governments and research communities worldwide is thatmisinterpretations due to poor modelling techniques will often result in biasedoutcomes for people with certain characteristics (e.g., race, socioeconomicstatus). In the New Zealand care and protection system, the over-representationof M\=aori might be incidentally intensified by predictive risk models leadingto possible cycles of bias towards M\=aori, ending disadvantaged ordiscriminated against, in decision-making policies. Ensuring these models canidentify the risk as accurately as possible and do not unintentionally add toan over-representation of M\=aori becomes a crucial matter. In this article weaddress this concern with the application of predictive risk modelling in theNew Zealand care and protection system. We study potential factors that mightimpact the accuracy and fairness of such statistical models along with possibleapproaches for improvement.

Potential Biased Outcomes on Child Welfare and Racial Minorities in New Zealand using Predictive Models: An Initial Review on Mitigation Approaches