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