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The U.S. Child Welfare System (CWS) is charged with improving outcomes forfoster youth; yet, they are overburdened and underfunded. To overcome thislimitation, several states have turned towards algorithmic decision-makingsystems to reduce costs and determine better processes for improving CWSoutcomes. Using a human-centered algorithmic design approach, we synthesize 50peer-reviewed publications on computational systems used in CWS to assess howthey were being developed, common characteristics of predictors used, as wellas the target outcomes. We found that most of the literature has focused onrisk assessment models but does not consider theoretical approaches (e.g.,child-foster parent matching) nor the perspectives of caseworkers (e.g., casenotes). Therefore, future algorithms should strive to be context-aware andtheoretically robust by incorporating salient factors identified by pastresearch. We provide the HCI community with research avenues for developinghuman-centered algorithms that redirect attention towards more equitableoutcomes for CWS.

A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System