A Systematic Review of Sophisticated Predictive and Prescriptive Analytics in Child Welfare: Accuracy, Equity, and Bias.
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| Title: | A Systematic Review of Sophisticated Predictive and Prescriptive Analytics in Child Welfare: Accuracy, Equity, and Bias. |
|---|---|
| Authors: | Hall, Seventy F.1 sfhall@buffalo.edu, Sage, Melanie1, Scott, Carol F.2, Joseph, Kenneth3 |
| Source: | Child & Adolescent Social Work Journal. Dec2024, Vol. 41 Issue 6, p831-847. 17p. |
| Subject Terms: | *Child welfare, *Social workers, *Data analysis, *Decision making, *Machine learning, *Algorithms, Risk assessment, Prediction models, Research funding, Data analytics, Descriptive statistics, Mann Whitney U Test, Systematic reviews, Statistics |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Child & Adolescent Social Work Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 180370148 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Systematic Review of Sophisticated Predictive and Prescriptive Analytics in Child Welfare: Accuracy, Equity, and Bias. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hall%2C+Seventy+F%2E%22">Hall, Seventy F.</searchLink><relatesTo>1</relatesTo><i> sfhall@buffalo.edu</i><br /><searchLink fieldCode="AR" term="%22Sage%2C+Melanie%22">Sage, Melanie</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Scott%2C+Carol+F%2E%22">Scott, Carol F.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Joseph%2C+Kenneth%22">Joseph, Kenneth</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Child+%26+Adolescent+Social+Work+Journal%22">Child & Adolescent Social Work Journal</searchLink>. Dec2024, Vol. 41 Issue 6, p831-847. 17p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Child+welfare%22">Child welfare</searchLink><br />*<searchLink fieldCode="DE" term="%22Social+workers%22">Social workers</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Mann+Whitney+U+Test%22">Mann Whitney U Test</searchLink><br /><searchLink fieldCode="DE" term="%22Systematic+reviews%22">Systematic reviews</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Child & Adolescent Social Work Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=180370148 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10560-023-00931-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 831 Subjects: – SubjectFull: Child welfare Type: general – SubjectFull: Social workers Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Decision making Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Risk assessment Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Research funding Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: Mann Whitney U Test Type: general – SubjectFull: Systematic reviews Type: general – SubjectFull: Statistics Type: general Titles: – TitleFull: A Systematic Review of Sophisticated Predictive and Prescriptive Analytics in Child Welfare: Accuracy, Equity, and Bias. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hall, Seventy F. – PersonEntity: Name: NameFull: Sage, Melanie – PersonEntity: Name: NameFull: Scott, Carol F. – PersonEntity: Name: NameFull: Joseph, Kenneth IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 07380151 Numbering: – Type: volume Value: 41 – Type: issue Value: 6 Titles: – TitleFull: Child & Adolescent Social Work Journal Type: main |
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