Algorithmic-Assisted Decision-Making Tools in Child Welfare Practice: A Systematic Review.
Purpose: Algorithmic-assisted decision-making tools are increasingly used in child welfare services, yet key factors and challenges for successful and ethical implementation remain underexplored. This review centers on fairness, equity, and ethics in their application. Methods: Using PRISMA guidelin...
| Publicado en: | Research on Social Work Practice Vol. 36; no. 4; pp. 382 - 398 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192851573&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192851573 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10497315 RSW jtl: Research on Social Work Practice issn: 10497315 maglogo: Y pubinfo: dt: May2026 vid: 36 iid: 4 pid: 344 pub: Sage Publications Inc. artinfo: ui: 192851573 10.1177/10497315251350933 ppf: 382 ppct: 16 formats: tig: atl: Algorithmic-Assisted Decision-Making Tools in Child Welfare Practice: A Systematic Review. aug: au: Yu, Meng-Hsuan Rose, Roderick A. affil: University of Maryland School of Social Work, Baltimore, MD, USA su: Child welfare Equality Social services Communication Needs assessment Access to information Decision support systems Risk assessment Professional practice Prediction models Grey literature Systematic reviews MEDLINE Prediction algorithms Machine learning Online information services Algorithms Psychology information storage & retrieval systems sug: subj: Child welfare Equality Social services Communication Needs assessment Access to information Other Individual and Family Services Decision support systems Risk assessment Professional practice Prediction models Grey literature Systematic reviews MEDLINE Prediction algorithms Machine learning Online information services Algorithms Psychology information storage & retrieval systems keyword: algorithmic fairness algorithmic-assisted decision-making child protective service child welfare machine learning model predictive risk modeling algorithmic fairness algorithmic-assisted decision-making child protective service child welfare machine learning model predictive risk modeling ab: Purpose: Algorithmic-assisted decision-making tools are increasingly used in child welfare services, yet key factors and challenges for successful and ethical implementation remain underexplored. This review centers on fairness, equity, and ethics in their application. Methods: Using PRISMA guidelines and including gray literature, nine studies were reviewed that described algorithmic tools implementation across various stages of the child welfare system. The review focused on identifying challenges and critical success factors, especially concerning fairness, equity, and ethics. Results: This study used a holistic framework to review critical aspects from tool development to deployment. Additionally, strategies to address fairness and ethical considerations were identified and synthesized. Discussion and Applications to Practice: Algorithmic-assisted tools hold promise for supporting high-stakes decisions in child welfare, but responsible use requires attention to ethical implementation. The review reveals significant methodological and empirical gaps, underscoring the need for future research to ensure equitable and effective deployment in practice. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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