Making accurate judgements in child welfare: comparing ChatGPT with qualified social workers

Wilkins, David ORCID: https://orcid.org/0000-0003-2780-0385 and Bennett, Verity ORCID: https://orcid.org/0000-0002-9311-4124 2025. Making accurate judgements in child welfare: comparing ChatGPT with qualified social workers. Child & Family Social Work 10.1111/cfs.13304

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Abstract

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.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Social Sciences (Includes Criminology and Education) Research Institutes & Centres > Children’s Social Care Research and Development Centre (CASCADE)
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Wiley
ISSN:	1356-7500
Date of First Compliant Deposit:	4 June 2025
Date of Acceptance:	11 April 2025
Last Modified:	04 Jun 2025 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/178748

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