That Which is Essential has been Made Invisible: The Need to Bring a Structural Risk Perspective to Reduce Racial Disproportionality in Child Welfare

Abstract

The racial and ethnic disproportionality and disparity in the child protective system (CPS) has been a concern for decades. Structural factors strongly influence engagement with the child welfare system and families experiencing poverty or financial hardship are at a heightened risk. The economic factors influencing child welfare involvement are further complicated by structural racism which has resulted in a greater prevalence of poverty and financial hardship for families who are Black, Native American or Alaska Native (Indigenous), or and Latino/Hispanic (Latino) and their communities. The multiple decision points within CPS are an opportunity to reify or correct for bias in child welfare outcomes. One major effort to eliminate racial disparities and disproportionalities has been to enact standardized decision-making procedures that aim to control for implicit or explicit bias in CPS. The Structured Decision-Making Model's (SDM) actuarial-based risk assessment (RA) is the gold-standard of these efforts. In this conceptual article, we ask (1) How are structural factors accounted for in assessment of risk within CPS? and (2) What are the consequences when structural factors are left out of risk assessments procedures? We posit that the exclusion of race, ethnicity, and economic factors from the RA has inflated the importance of variables that become proxies for these factors, resulting in inaccurate assessments of risk. The construction of this tool reflects how structural racism has been overlooked as an important cause of disproportionality in CPS, with interventions then focused on individual workers and cases, rather than the system at large. We suggest a new framework for thinking about risk, the structural risk perspective, and call for a revisioning of assessment of risk within child welfare that acknowledges the social determinants of CPS involvement.

Keywords: Actuarial-based risk assessment; Algorithms; Decision-making; Disparities; Disproportionality; Socioeconomic disadvantage; Structural racism.