I’ve written before about how one of the biggest losers in the 2016 elections was “predictive analytics.” All those algorithms kept assuring us that Hillary Clinton was all but certain to win. The media were suckered.
It turns out the media were not alone. In her review of a new book, Shattered: Inside Hillary Clinton’s Doomed Campaign, Michiko Kakutani of The New York Times writes that the campaign itself made the same disastrous error:
As described in “Shattered,” Clinton’s campaign manager, Robby Mook — who centered the Clinton operation on data analytics (information about voters, given to him by number crunchers) as opposed to more old-fashioned methods of polling, knocking on doors and trying to persuade undecideds — made one strategic mistake after another, but was kept on by Clinton, despite her own misgivings.
Yet “predictive analytics” continues to be sold, literally and figuratively, to child welfare systems as a way to target which parents should have their children taken away. In fact, as is discussed indetail here, predictive analytics magnifies the racial and class biases that are built into the child welfare.
It will work every bit as well in child welfare as it did in the Clinton campaign.