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Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks
Wilson Lukmanjaya;
Tony Butler;
Sarah Cox;
Oscar Perez-Concha;
Leah Bromfield;
George Karystianis
ABSTRACT
The use of child maltreatment text narratives represents an opportunity to strengthen the evidence base for child policy reform, yet they remain underutilized. Current research into child maltreatment often involves the use of structured data, potentially overlooking key details within the narratives that can be crucial to assist in developing effective interventions. Artificial Intelligence approaches such as large language models can assist in the large-scale analysis of child maltreatment narratives extracting useful population-level insights on indicators of mental health, and treatment needs. However, when applying such methods, a framework of five key steps can assist in their careful implementation to reduce risks of privacy and bias.
Citation
Please cite as:
Lukmanjaya W, Butler T, Cox S, Perez-Concha O, Bromfield L, Karystianis G
Leveraging AI to Investigate Child Maltreatment Text Narratives: Promising Benefits and Addressable Risks