How artificial intelligence and data-driven systems can improve child protection services

AI and data-driven systems are transforming child protection as they enable early detection of risk factors and more effective intervention. AI-driven tools survey online conversations, behavior patterns, and real-time whereabouts to enhance child safety, while cybersecurity and blockchain technology are increasingly used to protect the identity and personal data of children. Virtual reality supports safety education by simulating dangerous scenarios in controlled environments, and big data analytics help predict and prevent abuse or neglect through early warning systems. These innovations are particularly promising in contexts with limited human resources, where automated systems can augment decision-making and optimize resource allocation. However, challenges such as data privacy, algorithmic bias, inconsistent data quality, and lack of transparency remain significant barriers to full-scale implementation. This paper presents a review of academic literature, institutional reports, and real-world case studies, including tools like the Allegheny Family Screening Tool and mobile health platforms, to evaluate the effectiveness and limitations of current AI applications in child welfare. It also explores the ethical implications of predictive modeling and decision support systems, especially their impact on marginalized communities. Based on policy analysis and best practices, this research highlights key recommendations to improve the design, governance, and accountability of AI in child protection services. The work was developed within the framework of Subprogram 030101 "Strengthening the resilience, competitiveness, and sustainability of the economy of the Republic of Moldova in the context of the accession process to the European Union", institutional funding.