A Child Abuse Prediction Model Fails Poor Families

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Media Outlet: WIRED

WIRED published an excerpt from Virginia Eubanks' upcoming book, "Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor."

It's late November 2016, and I’m squeezed into the far corner of a long row of gray cubicles in the call screening center for the Allegheny County Office of Children, Youth and Families (CYF) child neglect and abuse hotline. I’m sharing a desk and a tiny purple footstool with intake screener Pat Gordon. We’re both studying the Key Information and Demographics System (KIDS), a blue screen filled with case notes, demographic data, and program statistics. We are focused on the records of two families: both are poor, white, and living in the city of Pittsburgh, Pennsylvania. Both were referred to CYF by a mandated reporter, a professional who is legally required to report any suspicion that a child may be at risk of harm from their caregiver. Pat and I are competing to see if we can guess how a new predictive risk model the county is using to forecast child abuse and neglect, called the Allegheny Family Screening Tool (AFST), will score them.
The stakes are high. According to the US Centers for Disease Control and Prevention, approximately one in four children will experience some form of abuse or neglect in their lifetimes. The agency’s Adverse Childhood Experience Study concluded that the experience of abuse or neglect has “tremendous, lifelong impact on our health and the quality of our lives,” including increased occurrences of drug and alcohol abuse, suicide attempts, and depression.

Author:

Virginia Eubanks is a Class of 2016 & 2017 New America Fellow, who pursued a three-year research study into digital privacy, economic inequality and data-based discrimination.