AI-Powered Predictive Analytics for Identifying Domestic Violence Risk Factors Across Cultures- An Overview - Ibiso, Isijola Bukola; OJo, Elizabeth Funmilayo; Ogunkorede, Olufunke Agatha; Afolalu, Adeniran Sunday (ABUAD Journal of Engineering Research and Development (AJERD), 2025)
This study offers a comprehensive examination of the use of AI-driven predictive analytics to discern risk variables for domestic violence across various cultural frameworks. Domestic violence (DV) is a widespread global concern with significant physical, psychological, and societal consequences, disproportionately affecting women while also influencing men. Conventional detection and intervention efforts are frequently reactive and inadequately funded, underscoring the necessity for innovative, data-driven approaches. Recent advancements in artificial intelligence (AI)—encompassing machine learning, natural language processing, and deep learning—present significant opportunities for enhanced timeliness and precision in risk assessment. These tools can assist in recognising patterns of abuse, forecasting escalation, and providing targeted support services instantaneously. Utilising AI in this delicate field necessitates meticulous attention to ethical dilemmas, encompassing privacy, data bias, and the possibility for technological exploitation. Furthermore, cultural norms, legal structures, and socioeconomic conditions can profoundly affect the occurrence of domestic abuse and the effectiveness of AI-driven remedies. This study highlights the significance of a culturally informed, ethically principled, and interdisciplinary methodology through the analysis of contemporary literature and practical implementations. Future research directions encompass the creation of more inclusive and transparent algorithms, the expansion of cross-cultural datasets, and the integration of AI into comprehensive public health and social services frameworks to guarantee safe and successful domestic violence prevention globally.