Click here to Download Weapons of Math Destruction PDF Book by Cathy O’Neil English having PDF Size 2.4 MB and No of Pages 202.
It was a hot August afternoon in 1946. Lou Boudreau, the playermanager of the Cleveland Indians, was having a miserable day. In the first game of a doubleheader, Ted Williams had almost singlehandedly annihilated his team. Williams, perhaps the game’s greatest hitter at the time, had smashed three home runs and driven home eight.
Weapons of Math Destruction PDF Book by Cathy O’Neil
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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.
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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.
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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 probability that they’ll win? In their hunt for answers, baseball statisticians have scrutinized every variable they can quantify and attached it to a value.
How much more is a double worth than a single? When, if ever, is it worth it to bunt a runner from first to second base? The answers to all of these questions are blended and combined into mathematical models of their sport. These are parallel universes of the baseball world, each a complex tapestry of probabilities.
They include every measurable relationship among every one of the sport’s components, from walks to home runs to the players themselves. The purpose of the model is to run different scenarios at every juncture, looking for the optimal combinations. If the Yankees bring in a righthanded pitcher to face Angels slugger Mike Trout, as compared to leaving in the current pitcher. Weapons of Math Destruction PDF Book
How much more likely are they to get him out? And how will that affect their overall odds of winning? Baseball is an ideal home for predictive mathematical modeling. As Michael Lewis wrote in his 2003 bestseller, Moneyball, the sport has attracted data nerds throughout its history. In decades past, fans would pore over the stats on the back of baseball cards.
Analyzing Carl Yastrzemski’s home run patterns or comparing Roger Clemens’s and Dwight Gooden’s strikeout totals. But starting in the 1980s, serious statisticians started to investigate what these figures, along with an avalanche of new ones, really meant: how they translated into wins, and how executives could maximize success with a minimum of dollars.
If you want a metaphor, one commonly used in this field comes from sausages. Think of the mortgages as little pieces of meat of varying quality, and think of the mortgage-backed securities as bundles of the sausage that result from throwing everything together and adding a bunch of strong spices. Weapons of Math Destruction PDF Book
Of course, sausages can vary in quality, and it’s hard to tell from the outside what went into them, but since they have a stamp from the USDA saying they’re safe to eat, our worries are put aside. As the world later learned, mortgage companies were making rich profits during the boom by loaning money to people for homes they couldn’t afford.
The strategy was simply to write unsustainable mortgages, snarf up the fees, and then unload the resulting securities— the sausages—into the booming mortgage security market. In one notorious case, a strawberry picker named Alberto Ramirez, who made $14,000 a year, managed to finance a $720,000 house in Rancho Grande, California.
His broker apparently told him that he could refinance in a few months and later flip the house and make a tidy profit. Months later, he defaulted on the loan. In the run-up to the housing collapse, mortgage banks were not only offering unsustainable deals but actively prospecting for victims in poor and minority neighborhoods. Weapons of Math Destruction PDF Book
In a federal lawsuit, Baltimore officials charged Wells Fargo with targeting black neighborhoods for so-called ghetto loans. The bank’s “emerging markets” unit, according to a former bank loan officer, Beth Jacobson, focused on black churches. The idea was that trusted pastors would steer their congregants toward loans.
These turned out to be subprime loans carrying the highest interest rates. The bank sold these even to borrowers with rock-solid credit, who should have qualified for loans with far better terms. By the time Baltimore filed the suit, in 2009, more than half of the properties subject to foreclosure on Well Fargo loans were empty, and 71 percent of them were in largely African American neighborhoods.
In 2012, Wells Fargo settled the suit, agreeing to pay $175 million to thirty thousand victims around the country.) To be clear, the subprime mortgages that piled up during the housing boom, whether held by strawberry pickers in California or struggling black congregants in Baltimore, were not WMDs. Weapons of Math Destruction PDF Book Download
They were financial instruments, not models, and they had little to do with math. (In fact, the brokers went to great lengths to ignore inconvenient numbers.) But when banks started loading mortgages like Alberto Ramirez’s into classes of securities and selling them, they were relying on flawed mathematical models to do it.
The risk model attached to mortgagebacked securities was a WMD. The banks were aware that some of the mortgages were sure to default. But banks held on to two false assumptions, which sustained their confidence in the system. But even those who claw their way into a top college lose out. If you think about it, the college admissions game, while lucrative for some, has virtually no educational value.
The complex and fraught production simply re-sorts and reranks the very same pool of eighteenyear-old kids in newfangled ways. They don’t master important skills by jumping through many more hoops or writing meticulously targeted college essays under the watchful eye of professional tutors. Others scrounge online for cut-rate versions of those tutors. Weapons of Math Destruction PDF Book Download
All of them, from the rich to the working class, are simply being trained to fit into an enormous machine—to satisfy a WMD. And at the end of the ordeal, many of them will be saddled with debt that will take decades to pay off. They’re pawns in an arms race, and it’s a particularly nasty one. So is there a fix?
During his second term, President Obama suggested coming up with a new college rankings model, one more in tune with national priorities and middle-class means than the U.S. News version. His secondary goal was to sap power from for-profit colleges (a money-sucking scourge that we’ll discuss in the next chapter.
Obama’s idea would be to tie a college ranking system to a different set of metrics, including affordability, the percentage of poor and minority students, and postgraduation job placement. Like the U.S. News ranking, it would also consider graduation rate. If colleges dipped below the minimums in these categories. Weapons of Math Destruction PDF Book Download
They’d get cut off from the $180 million-per-year federal student loan market (which the forprofit universities have been feasting on). All of those sound like worthy goals, to be sure, but every ranking system can be gamed. And when that happens, it creates new and different feedback loops and a host of unintended consequences.
If you think about it, hot-spot predictors are similar to the shifting defensive models in baseball that we discussed earlier. Those systems look at the history of each player’s hits and then position fielders where the ball is most likely to travel. Crime prediction software carries out similar analysis, positioning cops where crimes appear most likely to occur.
Both types of models optimize resources. But a number of the crime prediction models are more sophisticated, because they predict progressions that could lead to waves of crime. PredPol, for example, is based on seismic software: it looks at a crime in one area, incorporates it into historical patterns, and predicts when and where it might occur next. Weapons of Math Destruction PDF Book Download
One simple correlation it has found: if burglars hit your nextdoor neighbor’s house, batten down the hatches.) Predictive crime models like PredPol have their virtues. Unlike the crime-stoppers in Steven Spielberg’s dystopian movie Minority Report (and some ominous real-life initiatives, which we’ll get to shortly), the cops don’t track down people before they commit crimes.
Jeffrey Brantingham, the UCLA anthropology professor who founded PredPol, stressed to me that the model is blind to race and ethnicity. And unlike other programs, including the recidivism risk models we discussed, which are used for sentencing guidelines, PredPol doesn’t focus on the individual. Instead, it targets geography.
The key inputs are the type and location of each crime and when it occurred. That seems fair enough. And if cops spend more time in the high-risk zones, foiling burglars and car thieves, there’s good reason to believe that the community benefits. But most crimes aren’t as serious as burglary and grand theft auto, and that is where serious problems emerge. Weapons of Math Destruction PDF Book Free
When police set up their PredPol system, they have a choice. They can focus exclusively on socalled Part 1 crimes. These are the violent crimes, including homicide, arson, and assault, which are usually reported to them. But they can also broaden the focus by including Part 2 crimes, including vagrancy, aggressive panhandling, and selling and consuming small quantities of drugs.
Many of these “nuisance” crimes would go unrecorded if a cop weren’t there to see them. The police regarded stop and frisk as a filtering device for crime. The idea is simple. Police officers stop people who look suspicious to them. It could be the way they’re walking or dressed, or their tattoos. The police talk to them and size them up, often while they’re spread-eagled against a wall or the hood of a car.
They ask for their ID, and they frisk them. Stop enough people, the thinking goes, and you’ll no doubt stop loads of petty crimes, and perhaps some big ones. The policy, implemented by Mayor Michael Bloomberg’s administration, had loads of public support. Over the previous decade, the number of stops had risen by 600 percent, to nearly seven hundred thousand incidents. Weapons of Math Destruction PDF Book Free
The great majority of those stopped were innocent. For them, these encounters were highly unpleasant, even infuriating. Yet many in the public associated the program with the sharp decline of crime in the city. New York, many felt, was safer. And statistics indicated as much. Homicides, which had reached 2,245 in 1990, were down to 515 (and would drop below 400 by 2014.
Everyone knew that an outsized proportion of the people the police stopped were young, dark-skinned men. But how many did they stop? And how often did these encounters lead to arrests or stop crimes? While this information was technically public, much of it was stored in a database that was hard to access. The software didn’t work on our computers or flow into Excel spreadsheets.
Our job at the hackathon was to break open that program and free the data so that we could all analyze the nature and effectiveness of the stop-and-frisk program. What we found, to no great surprise, was that an overwhelming majority of these encounters—about 85 percent—involved young African American or Latino men. Weapons of Math Destruction PDF Book Free
In certain neighborhoods, many of them were stopped repeatedly. Only 0.1 percent, or one of one thousand stopped, was linked in any way to a violent crime. Yet this filter captured many others for lesser crimes, from drug possession to underage drinking, that might have otherwise gone undiscovered.
Some of the targets, as you might expect, got angry, and a good number of those found themselves charged with resisting arrest. Stop and frisk isn’t exactly a WMD, because it relies on human judgment and is not formalized into an algorithm. But it is built upon a simple and destructive calculation.
If police stop one thousand people in certain neighborhoods, they’ll uncover, on average, one significant suspect and lots of smaller ones. This isn’t so different from the longshot calculations used by predatory advertisers or spammers. Even when the hit ratio is miniscule, if you give yourself enough chances you’ll reach your target. Weapons of Math Destruction PDF Book Free
And that helps to explain why the program grew so dramatically under Bloomberg’s watch. If stopping six times as many people led to six times the number of arrests, the inconvenience and harassment suffered by thousands upon thousands of innocent people was justified. Weren’t they interested in stopping crime?
Aspects of stop and frisk were similar to WMDs, though. For example, it had a nasty feedback loop. It ensnared thousands of black and Latino men, many of them for committing the petty crimes and misdemeanors that go on in college frats, unpunished, every Saturday night. But while the great majority of university students were free to sleep off their excesses.
The victims of stop and frisk were booked, and some of them dispatched to the hell that is Rikers Island. What’s more, each arrest created new data, further justifying the policy. Weapons of Math Destruction PDF Book Free