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Predicting Prevention: Algorithmic Logics in the Child Welfare System
J. Khadijah Abdurahman, The American Assembly and Columbia University
in conversation with Christian Sandvig, University of Michigan
In response to the public outcry against the Allegheny Family Screening Tool (AFST), the pilot of predictive analytics in the US child welfare system to predict which child should be removed, state child welfare agencies have justified their use of automated decision systems as a way to “allocate preventive services to at risk communities”. We will move beyond the “garbage in garbage out” analysis of historical inequities baked into the data used by predictive risk models to reflect on what type of infrastructure is built up by algorithms in human services. What kind of resistance is possible to connect the tech labor struggles of engineers tasked with developing these systems and the people on the receiving end of these predictions?
J. Khadijah Abdurahman is based at We Be Imagining at Columbia University’s The American Assembly and the INCITE Center. I’m an abolitionist whose research focus is predictive analytics in the child welfare system, the impact of social media and surveillance in Ethiopia.