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

TL;DR

This paper examines how predictive risk models in New Zealand's child welfare system may unintentionally reinforce racial biases against Maori children, and explores mitigation strategies to improve fairness and accuracy.

Contribution

It provides an initial review of bias issues in predictive models for child welfare in New Zealand and discusses potential approaches to mitigate racial bias.

Findings

Predictive models may inadvertently reinforce Maori over-representation.

Bias in models can impact fairness and decision-making accuracy.

Mitigation approaches can help reduce racial disparities in outcomes.

Abstract

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.