Along the way author O'Neil examines how big data - mathematical models - are now used to determine who gets into college, how corporations target advertising to specific groups, whether you get and keep a job, and assessing credit ratings and insurance risk. Through insider interviews and personal experience, O'Neil documents how model building often integrates the inherent biases of the people building the models, as well as historical biases. In many different ways, and through dozens of pertinent examples, it becomes clear that WMDs are designed primarily to reduce costs and promote higher income for the companies that use them.
Worse, WMDs reinforce societal prejudices and stereotypes, targeting - even if sometimes unintentionally - the poor and minorities, further driving them downward and limiting opportunities for upward movement. The poor are kept poor by reducing access to affordable loans, depressing credit scores, and blocking job options through linkage to factors that are irrelevant or biased. And because these models are black boxes both to the people held back because of them and, often, the people administering and using them, there often is no way to even know why rejections have occurred. Without the model feedback seen in more useful models, these WMDs cause their destruction with no hope of ever improving the algorithms used.
O'Neil jumps from the financial crisis of 2008 to the removal of teachers unfairly to how Google and Facebook influence behaviors simply through their choice of what people see in their feeds - and who gets to see it.
As models, algorithms, automation of processes, and online data collection continue to become more prevalent, and potentially more destructive, this book becomes essential reading. Its valuable insights, whether you agree with everything the author suggests or not, are critical to our informed discussion of what we want our future to look like.
Available on Amazon.
