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