The World Health Organization estimates around 1 in 2 children worldwide experience physical, sexual, and emotional abuse and neglect [1]. In Australia, the monitoring of child maltreatment (ie, “all forms of physical and emotional ill-treatment, sexual abuse, neglect, and exploitation that results in actual or potential harm to the child’s health, development or dignity” [2,3]) relies on administrative data provided by child protection departments for local and national reporting purposes [4-6]. However, the structured nature of these data limits the reporting of more nuanced information such as maltreatment types, treatment needs, and mental health, preventing the ability to answer key questions. For example, the 2021 Australian Child Maltreatment Study [6] recorded the prevalence of physical abuse and sexual abuse at 32.0% and 28.5%, respectively, whereas the Australian Bureau of Statistics [5] in the same year reported that 1 in 7 (14%) children experienced physical and sexual abuse. Several factors contribute to such discrepancies, including the use of different methodologies (eg, self-report, cross-sectional surveys, and surveillance through administrative data), varying definitions of abuse and age ranges, and biases due to the small sample sizes and response rates [7-9]. While high-level reporting is important, both sources fail to provide a comprehensive picture of child maltreatment necessary for effective policy responses. Characteristics such as uncommon maltreatment subtypes and treatment needs are lacking. Indeed, the Australian Productivity Commission states that “with technological developments and advances in analytical techniques, not only is the volume of data being generated and collected growing, but so too is the scope to make use of data in innovative ways in every sphere of life” [10]. Child protection should be no exception to this.

Research in child maltreatment has predominantly utilized statistical and artificial intelligence (AI) approaches such as artificial neural networks and predictive risk modeling applied to structured child protection administrative data [11-15]. Studies on health administrative records focused on developing models to predict cases of sexual abuse [11], physical abuse [12], and overall maltreatment [13-15], with robust performance. Furthermore, information has been successfully extracted using text mining techniques such as word embeddings, bag-of-words, and feature selection from free-text health notes [16-19] to detect physical abuse.

Nevertheless, rarely tapped sources of unstructured information in this area are child maltreatment narratives containing detailed descriptive accounts of incidents or experiences of physical or emotional harm to a child. These document the initial reasons for coming to the attention of authorities (ie, intake report), descriptions of a child’s experience of abuse or neglect, psychologists’ assessments, police and medical records, and a detailed history of child protection involvement. They also describe abuse types, substance use and abuse, recorded injuries, mental and behavioral problems, physical health conditions, parental and family characteristics, and the types and conditions of the accommodation. Utilizing the information contained within the narratives could fill existing knowledge gaps by highlighting less common abusive behaviors (eg, social abuse, coercive control) and enriching definitions and measures of abuse types and mental illnesses [20-22]. A limited number of studies have used these narratives to automatically extract information. Victor et al [23] and Perron et al [24,25] applied machine learning algorithms such as random forest and k-nearest neighbors on caseworker summaries to identify domestic violence events and opioid-related maltreatment risks, respectively. In addition, Saxena et al [26] used topic modeling to explore the concept and definitions of “risk” on child maltreatment records with promising performance. Despite the advent of newer technologies to conduct text analysis such as large language models (LLMs; AI models that can understand and generate language), limited attempts have been made [27,28] to implement this approach. In particular, Perron et al [27] reported an F₁-score of 89%‐95% for their model to classify and extract substance-related problems, while more recently, Stoll et al [28] noted their approach to classify maltreatment subtype yielded a superior performance against human reviewers by 10%.

However, automatically mining information from child maltreatment records faces significant limitations [23-28]. Most importantly, the lack of sufficient training data restricts the AI model’s capacity to learn complex patterns [29] such as nuanced indicators of abuse. In addition, the limited contextual understanding (even when using LLMs), along with the inconsistencies in narrative style and language, as caseworkers may document—or omit—events of child maltreatment through various terms, descriptions, and writing style, can lead to inaccurate outputs and falsehoods [26]. This is particularly critical when analyzing data related to Indigenous populations requiring a specific cultural lens through which to examine the data. Relying exclusively on one data type (eg, administrative data) can also skew the results and present an inaccurate picture [30].

To manually process such voluminous and diverse information is extremely time consuming and not without significant challenges, including a lack of human resources to inspect large scale data and the associated high cost. AI pipelines, particularly applications of LLMs, offer a solution for efficiently processing vast amounts of child maltreatment narratives, comprehending the text for a broader range of tasks (ie, summarization, classification) [27,28,31], and offering potentially greater quality control due to its standardized approach compared to other (eg, qualitative) methods [32]. A primary concern of child protection services is to safeguard and monitor those in their care who may be at risk of maltreatment and neglect. Timely processing of many thousands of cases provides the possibility for professionals and policy makers to make faster decisions (eg, identifying clusters of abuse or mental illness using geographic areas) by predicting the severity of future abuse or allocating resources to high-prevalence communities and intervene earlier. Consequently, new evidence-based protocols for early intervention and prevention can be developed aimed at improving outcomes for children and families.

While appealing, AI applications come with security risks (eg, malware, data leakage), performance issues (eg, “AI hallucinations”), technical limitations (eg, black box bias), and ethical conundrums (eg, machine vs human decisions). Hidden malware or malicious code within unvetted AI algorithms with built-in third-party components could result in leaking sensitive information residing in child maltreatment narratives [33,34]. Child protection departments are extremely cautious about providing data access (making this data some of the most difficult to access) as children’s data privacy risks could expose them to further harm. When the data involve Indigenous populations, additional cultural considerations for access, governance, and interpretation are required. Even with data access approval, challenges related to narrative data quality, such as data omission, will persist as researchers have limited control over these factors. Cultural and linguistic biases, as well as selection bias in the training data, also pose risks for misinterpretation of findings. In particular, LLM performance can be vulnerable due to “hallucinations” or misinformation with models generating inaccurate or false conclusions. This is further amplified by black box bias [35-37], since the decision-making process is not visible or understandable by humans and, therefore, it is hard to conduct any detailed error analysis.

For these reasons, AI-generated misinformation can have larger implications due to false positives and negatives, fabrication of summarization, and misclassifications. In child maltreatment cases, this could lead to the misidentification of mental health disorders, abuse and neglect misclassification, and unnecessary family separation [38-42]. Falsehoods could be interpreted as gold standards. Therefore, child maltreatment policies require a decision framework that draws conclusions from both machines and humans, with the final decision relying on human expertise. While some may propose automated approaches can reduce human errors, bias, and the time required to process reports, replacing human decision-making with AI-powered approaches requires ethical protocols to be in place. Relying exclusively on algorithms and their outputs should be discouraged as they lack the complexities and subtleties of human reasoning and decision making [43-47]. Human judgement needs to take priority over AI outputs [48] and adhere to research ethics and legal standards [49-51]. Machine-made choices bear potentially severe consequences when it comes to enhancing the evidence base and policy translation leading to social injustice and population oversurveillance [48]. Considering the inherent risk of biases [52,53] (ie, underrepresented groups sampling bias) and the cost of hardware requirements, security protocols, and manual code inspection, it is difficult to apply such algorithms widely without the appropriate precautions.

Risk mitigation strategies are essential to ensure the appropriate application of automated approaches in areas such as child protection. First, using deidentified data and downloadable models from verified sources minimizes the risk of leaking private information. The incorporation of local LLMs could be set up using a virtual machine and offline to prohibit data access and the possibility of reidentifying personal records if information were to be siphoned off through hidden malware. False outcomes could be mitigated through improved quality of input data, further training to improve the model’s understanding and designing clear, precise instructions for the model (ie, design prompt) that steer its response towards accurate information [47,48]. Governance infrastructures are also necessary to ensure appropriate handling of such sensitive data and the generated findings, especially if these data involve Indigenous populations, which would require input from key stakeholder groups and people with lived experience to ensure culturally sensitive analysis and interpretation. Finally, pipelines need to have expert-in-the-loop architecture to improve the quality and reliability of results. Ensuring that pipelines powered up by LLMs operate transparently with domain experts’ input not only protects against biased decision-making but also aligns with efforts to safeguard personal data, as some algorithms can obscure the processes that may compromise fairness and privacy.

Existing research using AI approaches on child maltreatment narratives follows the establishment of the technical pipeline to extract information and obtain data access. Additional steps (eg, governance, data deidentification, researcher vetting) are also required to not only enhance the performance of such approaches but to limit security risks, ensure translatable outputs, and boost public confidence in such approaches. For these reasons, we propose a seven-step framework for processing child maltreatment narratives with LLMs or any black box algorithm: (1) data governance (including cultural considerations where appropriate), (2) researcher vetting, (3) data deidentification, (4) data access, (5) feasibility testing of baseline methods, (6) large-scale implementation of black box algorithms, and (7) domain expert result validation. These steps align with Gillingham’s [48] principles of algorithmic accountability in social work and existing policy guidelines on AI for children [54,55] that prioritize children’s safety, well-being, data privacy, and AI transparency. Although Gillingham’s principles are rooted in decision support tools, they are relevant due to the generated results potentially contributing to policy making and translation.

The seven steps include:

Our proposed 7-step framework offers a novel research pipeline specific to ethical and secure use of AI in child maltreatment research. By introducing data governance, the inclusion of stakeholders with lived experience and structured deidentification processes and feasibility testing, the framework prioritizes both ethical and cultural safety while promoting analytical rigor. Incorporating domain expert validation further enhances the interpretability, contextual relevance, and ethical soundness of AI outputs—elements that are largely absent in the current literature. An illustrative example of a brief case scenario is presented in Figure 1. While it is understood that the framework may not apply to all contexts—given that protocols can vary across countries and may not have Indigenous cultures—the core principles are common. Adhering to these principles supports data privacy and cultural sensitivity and helps mitigate the uncaptured context problem and bias mitigation within the field through domain expert result validation [57].

As the complexity and scale of child maltreatment increases and the volume of information expands, an opportunity exists to apply new and sophisticated approaches that rely on AI to strengthen the evidence base by processing large volumes of narrative data. However, with this opportunity come risks that need to be minimized. The implementation of AI methods, particularly black box methods such as LLMs, should follow a framework of seven key steps—(1) data governance, (2) researcher vetting, (3) data deidentification, (4) data access, (5) feasibility testing of baseline methods, (6) large-scale implementation of black box algorithms, and (7) domain expert result validation—to ensure culturally sensitive governance, minimization of privacy risks, improvement data analysis, and optimization of AI usability in strengthening the evidence base for child maltreatment. Further research is needed to evaluate the framework’s effectiveness and practice to improve policies and lead to better outcomes in child protection.