Most countries operate procedures to safeguard children, including removal from parents in serious cases. In England, care applications and numbers have risen sharply, however, with wide variations not explained by levels of socio-economic deprivation alone. Drawing on extensive research, it is asserted that actuarial decision tools more accurately estimate risks to children and are needed to achieve consistency, transparency, and best outcomes for children. To date, however, child protection has not achieved gains made within comparable professions through statistical methods. The reasons are examined. To make progress requires understanding why statistical tools exert effect and how professionals use them in practice. Deep-rooted psychological factors operating within uncertainty can frustrate processes implemented to counter those forces. Crucially, tools constitute evidence; their use and interpretation should not fall to one practitioner or professional body and modifications must be open to scrutiny and adjudication. We explore the potential of novel big data technology to address the difficulties identified through tools that are accurate, simple, and universally applied within child protection. When embraced by all parties to proceedings, especially parents and their advisors, despite societal fears, big data may promote transparency of social work and judicial decisions.

Social work case files hold rich detail about the lives and needs of vulnerable groups. Traditional case-reading studies to gain generalisable knowledge are resource-intensive, however, and sample sizes thereby limited. The advent of ‘big data’ technology, and vast repositories of centrally stored electronic records offer social work researchers novel alternatives, including data linkage and predictive risk modelling using administrative data. Free-text documents, however – including assessments, reports, and case chronologies – remain a largely untapped resource. This paper describes how 5000 social work court statements held by the Child and Family Court Advisory Support Service in England (Cafcass) were analysed using natural language processing (NLP) based on simple rules and mathematical principles. Thirteen factors relating to harm and risk to children involved in care proceedings in England were identified by automated computer techniques, and almost 90% agreement with professional readers achieved when the factors were clear-cut. The study represents an innovative approach for social work research on complex social problems. In conclusion, the paper discusses learning points; practical implications; future research avenues; and the technical and ethical challenges of NLP.

Artificial intelligence (AI) is widely used to support decision making and interventions, arguably saving time, reducing bias and improving decision accuracy. The profession must urgently appraise the potential and pitfalls of this rapidly developing technology. This challenge was addressed at the 2024 European Social Work Research Association conference in Vilnius, at which the Evidence into Practice Special Interest Group focused on three contemporary AI developments: (1) large language models (LLMs); (2) AI- and robot-supported interventions; and (3) predictive risk modelling (PRM). This short ‘Reflection, exchange and dialogue’ article outlines the presentations, issues discussed and further reflections. Although LLMs have an impressive ability to manipulate language, essential case detail and analysis remain human tasks. There are robot technologies already helping people in the domains of disability and eldercare, and AI ‘language robots’ are being used favourably in low-risk mental health contexts, providing a non-judgemental (non-human) and ever-available ‘listener’ and ‘advisor’. PRMs raise many conflicting views. The ‘black box’ of AI may ‘hide’ systemic bias, though proponents argue that humans are biased too, so perfection is not an appropriate comparator. Our conclusion is that a priority is to examine, shape and regulate the interface between humans and computer algorithms.