Can Algorithms Detect When Kids Are in Danger? 🧠

Can an Algorithm Tell When Kids Are in Danger?<br />In particular, he told me, the kids who were screened in were more likely to be found in<br />need of services, “so they appear to be screening in the kids who are at real risk.”<br />Having demonstrated in its first year of operation<br />that more high-risk cases are now being flagged for investigation, Allegheny’s Family Screening Tool is drawing interest from child-protection agencies around the country.<br />In August 2016, Allegheny County became the first jurisdiction in the United States, or anywhere else, to let a predictive-analytics algorithm — the same kind of sophisticated pattern analysis used in credit reports, the automated buying<br />and selling of stocks and the hiring, firing and fielding of baseball players on World Series-winning teams — offer up a second opinion on every incoming call, in hopes of doing a better job of identifying the families most in need of intervention.<br />Beginning in 2012, though, two pioneering social scientists working on opposite sides of the globe — Emily Putnam-Hornstein, of the University of Southern California,<br />and Rhema Vaithianathan, now a professor at the Auckland University of Technology in New Zealand — began asking a different question: Which families are most at risk and in need of help?<br />What predictive analytics provides is an opportunity to more uniformly and evenly look at all those variables.”<br />For two months following Emily Lankes’s visit to the home of the children who had witnessed an overdose death, she tried repeatedly<br />to get back in touch with the mother to complete her investigation — calling, texting, making unannounced visits to the home.<br />But the human brain is not that deft at harnessing and making sense of all that data.”<br />She and Putnam-Hornstein linked many dozens of data points — just about everything known to the county<br />about each family before an allegation arrived — to predict how the children would fare afterward.