Notes from Weapons of Math Destruction

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Weapons of Math Destruction

By Cathy O'Neil

Chapter 2: Shell Shocked: My Journey of Disillusionment

Page 55 @ November 9, 2019

I started out building models to anticipate the behavior of visitors to various travel websites. The key question was whether someone showing up at the Expedia site was just browsing or looking to spend money. Those who weren’t planning to buy were worth very little in potential revenue. So we would show them comparison ads for competing services such as Travelocity or Orbitz. If they clicked on the ad, it brought in a few pennies, which was better than nothing. However, we didn’t want to feed these ads to serious shoppers. In the worst case, we’d gain a dime of ad revenue while sending potential customers to rivals, where perhaps they’d spend thousands of dollars on hotel rooms in London or Tokyo. It would take thousands of ad views to make up for even a few hundred dollars in lost fees. So it was crucial to keep those people in house.


Chapter 3: Arms Race: Going to College

Page 71 @ November 9, 2019

The response to this crackdown on cheating was volcanic. Some two thousand stone-throwing protesters gathered in the street outside the school. They chanted, “We want fairness. There is no fairness if you don’t let us cheat.”

Chapter 5: Civilian Casualties: Justice in the Age of Big Data

Page 104 @ November 12, 2019

So fairness isn’t calculated into WMDs. And the result is massive, industrial production of unfairness . If you think of a WMD as a factory, unfairness is the black stuff belching out of the smoke stacks. It’s an emission, a toxic one.


Page 210 @ November 12, 2019

Like doctors, data scientists should pledge a Hippocratic Oath, one that focuses on the possible misuses and misinterpretations of their models. Following the market crash of 2008, two financial engineers, Emanuel Derman and Paul Wilmott, drew up such an oath. It reads: