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.

翻译：临床判断与预测风险模型的结合正日益协助社会工作者识别面临虐待风险的儿童，并建议当局采取潜在干预措施。全球政府及研究界普遍关注的一个关键问题是：因建模技术欠佳导致的错误解读，往往会对具有特定特征（如种族、社会经济地位）的人群产生偏差结果。在新西兰的照护与保护体系中，毛利人比例过高的问题可能因预测风险模型而意外加剧，从而在决策政策中导致对毛利人产生可能的偏差循环，最终使其处于不利或被歧视的境地。确保这些模型能够尽可能准确地识别风险，且无意中不加剧毛利人比例过高的现象，已成为关键议题。本文针对这一关切，探讨了预测风险模型在新西兰照护与保护体系中的应用，研究了可能影响此类统计模型准确性与公平性的潜在因素，以及可能的改进方法。