Weapons of Math Destruction

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BOMB PARTS
What Is a Model? 

It was a hot August afternoon in 1946. Lou Boudreau, the player-manager of the Cleveland Indians, was having a miserable day. In the first game of a doubleheader, Ted Williams had almost single-handedly annihilated his team. Williams, perhaps the game s greatest hitter at the time, had smashed three home runs and driven home eight. The Indians ended up losing 11 to 10.

Boudreau had to take action. So when Williams came up for the first time in the second game, players on the Indians side started moving around. Boudreau, the shortstop, jogged over to where the second baseman would usually stand, and the second baseman backed into short right field. The third baseman moved to his left, into the shortstop s hole. It was clear that Boudreau, perhaps out of desperation, was shifting the entire orientation of his defense in an attempt to turn Ted Williams s hits into outs.

In other words, he was thinking like a data scientist. He had analyzed crude data, most of it observational: Ted Williams usually hit the ball to right field. Then he adjusted. And it worked. Fielders caught more of Williams s blistering line drives than before (though they could do nothing about the home runs sailing over their heads).

If you go to a major league baseball game today, you ll see that defenses now treat nearly every player like Ted Williams. While Boudreau merely observed where Williams usually hit the ball, managers now know precisely where every player has hit every ball over the last week, over the last month, throughout his career, against left-handers, when he has two strikes, and so on. Using this historical data, they analyze their current situation and calculate the positioning that is associated with the highest probability of success. And that sometimes involves moving players far across the field.

Shifting defenses is only one piece of a much larger question: What steps can baseball teams take to maximize the

Cathy O'Neil

O Neil s book offers a frightening look at how algorithms are increasingly regulating people. . . . Her knowledge of the power and risks of mathematical models, coupled with a gift for analogy, makes her one of the most valuable observers of the continuing weaponization of big data. . . . [She] does a masterly job explaining the pervasiveness and risks of the algorithms that regulate our lives. The New York Times Book Review

"Weapons of Math Destruction is the Big Data story Silicon Valley proponents won't tell. . . . [It] pithily exposes flaws in how information is used to assess everything from creditworthiness to policing tactics . . . a thought-provoking read for anyone inclined to believe that data doesn't lie. Reuters

This is a manual for the twenty-first century citizen, and it succeeds where other big data accounts have failed it is accessible, refreshingly critical and feels relevant and urgent. Financial Times

"Insightful and disturbing." New York Review of Books

Weapons of Math Destruction is an urgent critique of . . . the rampant misuse of math in nearly every aspect of our lives. Boston Globe

A fascinating and deeply disturbing book. Yuval Noah Harari, author of Sapiens

Illuminating . . . [O Neil] makes a convincing case that this reliance on algorithms has gone too far. The Atlantic

A nuanced reminder that big data is only as good as the people wielding it. Wired

If you ve ever suspected there was something baleful about our deep trust in data, but lacked the mathematical skills to figure out exactly what it was, this is the book for you. Salon

O Neil is an ideal person to write this book. She is an academic mathematician turned Wall Street quant turned data scientist who has been involved in Occupy Wall Street and recently started an algorithmic auditing company. She is one of the