Saturday, 7 May 2016

Big data and predictive analytics

"For some parents, the words `Child Protective Services` send chills down the spine. In an effort to stamp out fraud and abuse, welfare outfits across the country have honed in on poor communities of color for decades now, critics claim, with almost any government interaction potentially leading to kids being removed from their homes."

"Now big data is set [to] make the dynamic even more intenseand racially charged."

"In 1999, the foster care population in the United States reached a peak at 567,000. But due to both diminishing budgets and programs aimed at keeping children in their homes, that number dropped to 415,000 by 2014. The representation of black children in the foster system remains disproportionately high, however: Black children account for 24 percent of kids in foster care, while comprising just 14 percent of the general population of children in the US. And a burgeoning method for determining exactly which families get visits from child welfare caseworkers has advocates for low-income families worried the disparity will only get worse."

"The new approach is called `predictive analytics,` and it's taking the child welfare system by storm. Across the country, from suburban counties in Florida to major cities like Los Angeles, child welfare agencies are launching initiatives that take data points like race, parental welfare status, and criminal history, and a variety of other publicly available characteristics, and feed them into an algorithm that assigns each child a `risk` score. That score is then considered when determining whether a caseworker should visit a family."

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