One Bad Formula Can Spoil Everything: A Simple Adjustment That Would Improve the UN’s Gender Inequality Index
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There can be dangers as well as benefits in using mathematical methods to answer questions that have a large human component. In the middle of the last century, Darrell Huff  wrote of the myriad ways that statistics are used in marketing and politics to mislead the public. In our time, Felix Salmon  has described how David Li’s mathematical model for collateralized debt obligations was seized upon by Wall Street as a justification for irresponsible investment practices that precipitated the financial crisis of 2008; John Ewing  has decried the misuse of mathematics to evaluate teachers in his exposé of value-added models; and Cathy O’Neil  has explained many of the ways that data science has been used to undermine democratic values—for example, by reinstituting a type of racial discrimination in lending (called “redlining”) that had supposedly been outlawed in the United States fifty years ago.
Even those who use mathematical methods with the best of intentions to help...
We wish to thank John Sylvester for explaining Tikhonov regularization, Edith Seier and Jorge Estrada Jr. for helpful comments, Ann Hibner Koblitz for editorial assistance, and an anonymous reviewer for useful suggestions.
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