ABSTRACT The use of artificial intelligence (AI) to analyse big data in child protection holds significant potential for enhancing early detection, risk prediction and intervention in child abuse cases. This qualitative study explores the use of AI‐driven big data in child welfare in South Korea by exploring the perspectives of 10 child protection experts from public child protective services, data science and academia, recruited through purposive and snowball sampling. Thematic analysis identified AI's potential to improve the identification of at‐risk children, enhance data‐driven decision‐making and detect patterns of re‐abuse, contributing to more proactive intervention. However, substantial challenges emerged, including low predictive accuracy, the absence of qualitative and contextual data, misalignment between predictive systems and service delivery, workforce and operational constraints and gaps in follow‐up support. Ethical concerns—particularly regarding privacy, surveillance and discriminatory impacts on economically disadvantaged families—were also highlighted. Although participants viewed AI as a valuable tool that provides foundational data and strengthens early intervention, they emphasized the need for human judgment clearer operational guidelines, improved data literacy and integrated service systems. To ensure human‐cantered and ethical implementation, robust governance frameworks and sustained investment in service infrastructure are essential.
Kim et al. (Sun,) studied this question.