A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System
This work addresses the problem of algorithmic bias and lack of human-centered design in child welfare systems for policymakers and HCI researchers, though it is incremental as it synthesizes existing literature without new empirical results.
The paper reviewed 50 publications on algorithmic decision-making systems in the U.S. Child Welfare System, finding that most focus on risk assessment but neglect theoretical approaches and caseworker perspectives, and it suggests future algorithms should be context-aware and theoretically robust.
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.