Making Accurate Judgements in Child Welfare
Comparing ChatGPT With Qualified Social Workers
Bibliographic Data
| ID | 12569879 |
|---|---|
| Authors | David Wilkins (0000-0003-2780-0385, CASCADE Cardiff University Cardiff UK, corresponding author), Verity Bennett (0000-0002-9311-4124, CASCADE Cardiff University Cardiff UK) |
| Year | 2025 |
| Volume | 31 |
| Issue | 2 |
| Pages | 703-712 |
| Publication date | 2025-06-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Child & Family Social Work (JOURNAL) |
| Journal identifiers | ISSN: 1356-7500 • E-ISSN: 1365-2206 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/cfs.13304 |
| OpenAlex | W4410970094 |
| Language | EN |
| Citations received | 2 |
| References cited | 41 |
This study compares the judgemental accuracy of child and family social workers ( n = 581) with ChatGPT, a generative AI model. Using 12 anonymized referrals, participants were asked predictive questions to evaluate accuracy through Brier scores. ChatGPT outperformed the average social worker on 11 of the 12 referrals, though the difference was not statistically significant. These findings highlight the potential and the limitations for AI to support decision‐making in social work while emphasising the need to address ethical concerns and AI's inadequacies for understanding complex human needs and social contexts. The study contributes to ongoing discussions on integrating AI into social work, advocating for a balanced approach that enhances effectiveness while preserving the profession's essential human elements
Political science · Social Welfare · Welfare · Applied Psychology · Grief, Bereavement, and Mental Health · Law · Optimism, Hope, and Well-being · Palliative Care and End-of-Life Issues · Psychology · Social Psychology · Social Work
Knowledge, Truth and Reflexivity
Common errors of reasoning in child protection work
On the reality of cognitive illusions.
Algorithm appreciation
How To Do Things With Words
The Incoherence of Empiricism
AI Creates the Message
Automated Identification of Domestic Violence in Written Child Welfare Records
Decision‐making under uncertainty in child protection
Patterns of re‐referral to social services
Artificial Intelligence and Implications for the Australian Social Work Journal
Assessing parenting capacity in a child welfare context
Child welfare and future assessments – An analysis of discretionary decision-making in newborn removals in Norway
Machine Learning for Social Services
Professional Curiosity in Child Protection
Problematising Artificial Intelligence in Social Work Education
Knowledge and Reasoning in Social Work
The Information Behaviour of Social Workers
Revisiting the Rule of Optimism
Family Group Conference Provision in UK Local Authorities and Associations with Children Looked after Rates
Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users
Improving Reunification Practice
Democratising the Family and the State? The Case of Family Group Conferences in Child Welfare
The Value of Precision in Probability Assessment
Intentionality
Decision fatigue
The case for motivated reasoning
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |