A Systematic Review of Sophisticated Predictive and Prescriptive Analytics in Child Welfare: Accuracy, Equity, and Bias.
Child welfare agencies increasingly use machine learning models to predict outcomes and inform decisions. These tools are intended to increase accuracy and fairness but can also amplify bias. This systematic review explores how researchers addressed ethics, equity, bias, and model performance in the...
| Published in: | Child & Adolescent Social Work Journal Vol. 41; no. 6; pp. 831 - 848 |
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| Main Authors: | , , , |
| Format: | Article |
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Springer Nature
Dec2024
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=180370148&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 180370148 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07380151 COK jtl: Child & Adolescent Social Work Journal issn: 07380151 maglogo: N pubinfo: dt: Dec2024 vid: 41 iid: 6 pid: 237 pub: Springer Nature artinfo: ui: 180370148 10.1007/s10560-023-00931-2 ppf: 831 ppct: 17 formats: fmt: @attributes: type: P size: 1.3MB tig: atl: A Systematic Review of Sophisticated Predictive and Prescriptive Analytics in Child Welfare: Accuracy, Equity, and Bias. aug: au: Hall, Seventy F. Sage, Melanie Scott, Carol F. Joseph, Kenneth affil: https://ror.org/01y64my43 School of Social Work, University at Buffalo, 685 Baldy Hall, 14260, Buffalo, NY, USA https://ror.org/00jmfr291 School of Information, University of Michigan, Ann Arbor, MI, USA https://ror.org/01y64my43 Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA su: Child welfare Social workers Decision making Risk assessment Prediction models Data analysis Research funding Data analytics Descriptive statistics Mann Whitney U Test Systematic reviews Statistics Machine learning Algorithms sug: subj: Child welfare Social workers Decision making Risk assessment Prediction models Data analysis Research funding Data analytics Descriptive statistics Mann Whitney U Test Systematic reviews Statistics Machine learning Algorithms keyword: Child welfare workers Policy Predictive analytics Systematic review Child welfare workers Policy Predictive analytics Systematic review ab: Child welfare agencies increasingly use machine learning models to predict outcomes and inform decisions. These tools are intended to increase accuracy and fairness but can also amplify bias. This systematic review explores how researchers addressed ethics, equity, bias, and model performance in their design and evaluation of predictive and prescriptive algorithms in child welfare. We searched EBSCO databases, Google Scholar, and reference lists for journal articles, conference papers, dissertations, and book chapters published between January 2010 and March 2020. Sources must have reported on the use of algorithms to predict child welfare-related outcomes and either suggested prescriptive responses, or applied their models to decision-making contexts. We calculated descriptive statistics and conducted Mann-Whitney U tests, and Spearman's rank correlations to summarize and synthesize findings. Of 15 articles, fewer than half considered ethics, equity, or bias or engaged participatory design principles as part of model development/evaluation. Only one-third involved cross-disciplinary teams. Model performance was positively associated with number of algorithms tested and sample size. No other statistical tests were significant. Interest in algorithmic decision-making in child welfare is growing, yet there remains no gold standard for ameliorating bias, inequity, and other ethics concerns. Our review demonstrates that these efforts are not being reported consistently in the literature and that a uniform reporting protocol may be needed to guide research. In the meantime, computer scientists might collaborate with content experts and stakeholders to ensure they account for the practical implications of using algorithms in child welfare settings. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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