Using Artificial Intelligence to Detect Risk of Family Violence: Protocol for a Systematic Review and Meta-Analysis

Background: Despite the implementation of prevention strategies, family violence continues to be a prevalent issue worldwide. Current strategies to reduce family violence have demonstrated mixed success and innovative approaches are needed urgently to prevent the occurrence of family violence. Incorporating artificial intelligence (AI) into prevention strategies is gaining research attention, particularly the use of textual or voice signal data to detect individuals at risk of perpetrating family violence. However, no review to date has collated extant research regarding how accurate AI is at identifying individuals who are at risk of perpetrating family violence.

Objective: The primary aim of this systematic review and meta-analysis is to assess the accuracy of AI models in differentiating between individuals at risk of engaging in family violence versus those who are not using textual or voice signal data.

Methods: The following databases will be searched from conception to the search date: IEEE Xplore, PubMed, PsycINFO, EBSCOhost (Psychology and Behavioral Sciences collection), and Computers and Applied Sciences Complete. ProQuest Dissertations and Theses A&I will also be used to search the grey literature. Studies will be included if they report on human adults and use machine learning to differentiate between low and high risk of family violence perpetration. Studies may use voice signal data or linguistic (textual) data and must report levels of accuracy in determining risk. In the data screening and full-text review and quality analysis phases, 2 researchers will review the search results and discrepancies and decisions will be resolved through masked review of a third researcher. Results will be reported in a narrative synthesis. In addition, a random effects meta-analysis will be conducted using the area under the receiver operating curve reported in the included studies, assuming sufficient eligible studies are identified. Methodological quality of included studies will be assessed using the risk of bias tool in nonrandomized studies of interventions.

Results: As of October 2024, the search has not commenced. The review will document the state of the research concerning the accuracy of AI models in detecting the risk of family violence perpetration using textual or voice signal data. Results will be presented in the form of a narrative synthesis. Results of the meta-analysis will be summarized in tabular form and using a forest plot.

Conclusions: The findings from this study will clarify the state of the literature on the accuracy of machine learning models to identify individuals who are at high risk of perpetuating family violence. Findings may be used to inform the development of AI and machine learning models that can be used to support possible prevention strategies.

Trial registration: PROSPERO CRD42023481174; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=481174.

International registered report identifier (irrid): PRR1-10.2196/54966.