000 03291nab a22003617a 4500
999 _c8199
_d8199
005 20250625151639.0
008 230530s2023 ||||| |||| 00| 0 eng d
040 _aAFVC
100 _aHall, Seventy F.
_911989
245 _aA systematic review of sophisticated predictive and prescriptive analytics in child welfare :
_baccuracy, equity and bias
_cSeventy F. Hall, Melanie Sage, Carol F. Scott and Kenneth Joseph
260 _bSpringer,
_c2023
500 _aChild and Adolescent Social Work Journal, 2023, First published online, 23 May 2023
520 _aChild 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. (Authors' abstract). New Zealand projects are included in this review. Record #8199
650 _aCHILD ABUSE
_9103
650 _aCHILD PROTECTION
_9118
650 _aCHILD WELFARE
_9124
650 _aDATA ANALYSIS
_9181
650 _aETHICS
_95807
650 0 _94928
_aPREDICTIVE RISK MODELLING
650 0 _aSOCIAL WORK PRACTICE
_9562
650 0 _aSYSTEMATIC REVIEWS
_93140
651 _aINTERNATIONAL
_93624
651 4 _aUNITED STATES
_92646
651 0 _95203
_aTHE NETHERLANDS
651 4 _aNEW ZEALAND
_92588
700 _aSage, Melanie
_911990
700 _aScott, Carol F.
_911991
700 _aJoseph, Kenneth
_911992
773 0 _tChild and Adolescent Social Work Journal, 2023, First published online, 23 May 2023
830 _aChild and Adolescent Social Work Journal
_97820
856 _uhttps://doi.org/10.1007/s10560-023-00931-2
_zDOI: 10.1007/s10560-023-00931-2
942 _2ddc
_cARTICLE
_hnews120