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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Hardcover – September 6, 2016
Cathy O'Neil (Author) Find all the books, read about the author, and more. See search results for this author |
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A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life and threaten to rip apart our social fabric.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we get a car loan, how much we pay for health insurance—are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.
But as Cathy O’Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.
Tracing the arc of a person’s life, O’Neil exposes the black box models that shape our future, both as individuals and as a society. These “weapons of math destruction” score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health.
O’Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.
- Print length272 pages
- LanguageEnglish
- PublisherCrown
- Publication dateSeptember 6, 2016
- Dimensions5.79 x 1.09 x 8.54 inches
- ISBN-100553418815
- ISBN-13978-0553418811
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Editorial Reviews
Review
A Boston Globe Best Book of 2016
One of Wired's Required Reading Picks of 2016
One of Fortune's Favorite Books of 2016
A Kirkus Reviews Best Book of 2016
A Chicago Public Library Best Book of 2016
A Nature.com Best Book of 2016
An On Point Best Book of 2016
New York Times Editor's Choice
A Maclean's Bestseller
Winner of the 2016 SLA-NY PrivCo Spotlight Award
“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.”
—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 21st-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; The Guardian’s Best Books of 2016
“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 strongest voices speaking out for limiting the ways we allow algorithms to influence our lives… While Weapons of Math Destruction is full of hard truths and grim statistics, it is also accessible and even entertaining. O’Neil’s writing is direct and easy to read—I devoured it in an afternoon.”
—Scientific American
“Readable and engaging… succinct and cogent… Weapons of Math Destruction is The Jungle of our age… [It] should be required reading for all data scientists and for any organizational decision-maker convinced that a mathematical model can replace human judgment."
—Mark Van Hollebeke, Data and Society: Points
“Indispensable… Despite the technical complexity of its subject, Weapons of Math Destruction lucidly guides readers through these complex modeling systems… O’Neil’s book is an excellent primer on the ethical and moral risks of Big Data and an algorithmically dependent world… For those curious about how Big Data can help them and their businesses, or how it has been reshaping the world around them, Weapons of Math Destruction is an essential starting place.”
—National Post
“Cathy O’Neil has seen Big Data from the inside, and the picture isn’t pretty. Weapons of Math Destruction opens the curtain on algorithms that exploit people and distort the truth while posing as neutral mathematical tools. This book is wise, fierce, and desperately necessary.”
—Jordan Ellenberg, University of Wisconsin-Madison, author of How Not To Be Wrong
“O’Neil has become [a whistle-blower] for the world of Big Data… [in] her important new book… Her work makes particularly disturbing points about how being on the wrong side of an algorithmic decision can snowball in incredibly destructive ways.”
—TIME
“O’Neil’s work is so important… [her] book is a vital crash-course in the specialized kind of statistical knowledge we all need to interrogate the systems around us and demand better.”
—Boing Boing
“Cathy O’Neil, a number theorist turned data scientist, delivers a simple but important message: Statistical models are everywhere, and they exert increasing power over many aspects of our daily lives… Weapons of Math Destruction provides a handy map to a few of the many areas of our lives over which invisible algorithms have gained some control. As the empire of big data continues to expand, Cathy O’Neil’s reminder of the need for vigilance is welcome and necessary.”
—American Prospect
“An avowed math nerd, O’Neil has written an engaging description of the effect of crunched data on our lives.”
—Hicklebee’s, San Francisco Chronicle
“By tracking how algorithms shape people's lives at every stage, O'Neil makes a compelling case that our bot overlords are using data to discriminate unfairly and foreclose democratic choices. If you work with data, or just produce reams of it online, this is a must-read.”
—ArsTechnica
“Lucid, alarming, and valuable… [O’Neil’s] writing is crisp and precise as she aims her arguments to a lay audience. This makes for a remarkably page-turning read for a book about algorithms. Weapons of Math Destruction should be required reading for anybody whose life will be affected by Big Data, which is to say: required reading for everyone. It’s a wake-up call – a journalistic heir to The Jungle and Silent Spring. Like those books, it should change the course of American society.”
—Aspen Times
"[O'Neil's] propulsive study reveals many models that are currently 'micromanaging' the US economy as opaque and riddled with bias."
—Nature
“You don’t need to be a nerd to appreciate the significance of [O’Neil’s] message… Weapons is a must-read for anyone who is working to combat economic and racial discrimination.”
—Goop
"Cathy O’Neil’s book... is important and covers issues everyone should care about. Bonus points: it’s accessible, compelling, and—something I wasn’t expecting—really fun to read.”
—Inside Higher Ed
“Often we don’t even know where to look for those important algorithms, because by definition the most dangerous ones are also the most secretive. That’s why the catalogue of case studies in O’Neil’s book are so important; she’s telling us where to look.”
—The Guardian
“O’Neil is passionate about exposing the harmful effects of Big Data–driven mathematical models (what she calls WMDs), and she’s uniquely qualified for the task… [She] makes a convincing case that many mathematical models today are engineered to benefit the powerful at the expense of the powerless… [and] has written an entertaining and timely book that gives readers the tools to cut through the ideological fog obscuring the dangers of the Big Data revolution.”
—In These Times
“In this simultaneously illuminating and disturbing account, [O’Neil] describes the many ways in which widely used mathematic models—based on ‘prejudice, misunderstanding, and bias’—tend to punish the poor and reward the rich… She convincingly argues for both more responsible modeling and federal regulation. An unusually lucid and readable look at the daunting algorithms that govern so many aspects of our lives.”
—Kirkus Reviews (starred)
“Even as a professional mathematician, I had no idea how insidious Big Data could be until I read Weapons of Math Destruction. Though terrifying, it’s a surprisingly fun read: O’Neil’s vision of a world run by algorithms is laced with dark humor and exasperation—like a modern-day Dr. Strangelove or Catch-22. It is eye-opening, disturbing, and deeply important.”
—Steven Strogatz, Cornell University, author of The Joy of x
“This taut and accessible volume, the stuff of technophobes’ nightmares, explores the myriad ways in which largescale data modeling has made the world a less just and equal place. O’Neil speaks from a place of authority on the subject… Unlike some other recent books on data collection, hers is not hysterical; she offers more of a chilly wake-up call as she walks readers through the ways the ‘big data’ industry has facilitated social ills such as skyrocketing college tuitions, policing based on racial profiling, and high unemployment rates in vulnerable communities… eerily prescient.”
—Publishers Weekly
"Well-written, entertaining and very valuable."
—Times Higher Education
"Not math heavy, but written in an exceedingly accessible, almost literary style; [O'Neil's] fascinating case studies of WMDs fit neatly into the genre of dystopian literature. There's a little Philip K. Dick, a little Orwell, a little Kafka in her portrait of powerful bureaucracies ceding control of the most intimate decisions of our lives to hyper-empowered computer models riddled with all of our unresolved, atavistic human biases."
—Paris Review
“Through harrowing real-world examples and lively story-telling, Weapons of Math Destruction shines invaluable light on the invisible algorithms and complex mathematical models used by government and big business to undermine equality and increase private power. Combating secrecy with clarity and confusion with understanding, this book can help us change course before it’s too late.”
—Astra Taylor, author of The People’s Platform: Taking Back Power and Culture in the Digital Age
"Weapons of Math Destruction is a fantastic, plainspoken call to arms. It acknowledges that models aren't going away: As a tool for identifying people in difficulty, they are amazing. But as a tool for punishing and disenfranchising, they're a nightmare.”
—Cory Doctorow, author of Little Brother and co-editor of Boing Boing
“Many algorithms are slaves to the inequalities of power and prejudice. If you don’t want these algorithms to become your masters, read Weapons of Math Destruction by Cathy O’Neil to deconstruct the latest growing tyranny of an arrogant establishment.”
—Ralph Nader, author of Unsafe at Any Speed
“In this fascinating account, Cathy O'Neil leverages her expertise in mathematics and her passion for social justice to poke holes in the triumphant narrative of Big Data. She makes a compelling case that math is being used to squeeze marginalized segments of society and magnify inequities. Her analysis is superb, her writing is enticing, and her findings are unsettling.”
—danah boyd, founder of Data & Society and author of It’s Complicated
"From getting a job to finding a spouse, predictive algorithms are silently shaping and controlling our destinies. Cathy O'Neil takes us on a journey of outrage and wonder, with prose that makes you feel like it's just a conversation. But it’s an important one. We need to reckon with technology.”
—Linda Tirado, author ofHand to Mouth: Living in Bootstrap America
“Next time you hear someone gushing uncritically about the wonders of Big Data, show them Weapons of Math Destruction. It’ll be salutary.”
—Felix Salmon, Fusion
About the Author
Excerpt. © Reprinted by permission. All rights reserved.
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 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 right-handed pitcher to face Angels slugger Mike Trout, as compared to leaving in the current pitcher, 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.
“Moneyball” is now shorthand for any statistical approach in domains long ruled by the gut. But baseball represents a healthy case study—and it serves as a useful contrast to the toxic models, or WMDs, that are popping up in so many areas of our lives. Baseball models are fair, in part, because they’re transparent. Everyone has access to the stats and can understand more or less how they’re interpreted. Yes, one team’s model might give more value to home run hitters, while another might discount them a bit, because sluggers tend to strike out a lot. But in either case, the numbers of home runs and strikeouts are there for everyone to see.
Baseball also has statistical rigor. Its gurus have an immense data set at hand, almost all of it directly related to the performance of players in the game. Moreover, their data is highly relevant to the outcomes they are trying to predict. This may sound obvious, but as we’ll see throughout this book, the folks building WMDs routinely lack data for the behaviors they’re most interested in. So they substitute stand-in data, or proxies. They draw statistical correlations between a person’s zip code or language patterns and her potential to pay back a loan or handle a job. These correlations are discriminatory, and some of them are illegal. Baseball models, for the most part, don’t use proxies because they use pertinent inputs like balls, strikes, and hits.
Most crucially, that data is constantly pouring in, with new statistics from an average of twelve or thirteen games arriving daily from April to October. Statisticians can compare the results of these games to the predictions of their models, and they can see where they were wrong. Maybe they predicted that a left-handed reliever would give up lots of hits to right-handed batters—and yet he mowed them down. If so, the stats team has to tweak their model and also carry out research on why they got it wrong. Did the pitcher’s new screwball affect his statistics? Does he pitch better at night? Whatever they learn, they can feed back into the model, refining it. That’s how trustworthy models operate. They maintain a constant back-and-forth with whatever in the world they’re trying to understand or predict. Conditions change, and so must the model.
Now, you may look at the baseball model, with its thousands of changing variables, and wonder how we could even be comparing it to the model used to evaluate teachers in Washington, D.C., schools. In one of them, an entire sport is modeled in fastidious detail and updated continuously. The other, while cloaked in mystery, appears to lean heavily on a handful of test results from one year to the next. Is that really a model?
The answer is yes. A model, after all, is nothing more than an abstract representation of some process, be it a baseball game, an oil company’s supply chain, a foreign government’s actions, or a movie theater’s attendance. Whether it’s running in a computer program or in our head, the model takes what we know and uses it to predict responses in various situations. All of us carry thousands of models in our heads. They tell us what to expect, and they guide our decisions.
Here’s an informal model I use every day. As a mother of three, I cook the meals at home—my husband, bless his heart, cannot remember to put salt in pasta water. Each night when I begin to cook a family meal, I internally and intuitively model everyone’s appetite. I know that one of my sons loves chicken (but hates hamburgers), while another will eat only the pasta (with extra grated parmesan cheese). But I also have to take into account that people’s appetites vary from day to day, so a change can catch my model by surprise. There’s some unavoidable uncertainty involved.
The input to my internal cooking model is the information I have about my family, the ingredients I have on hand or I know are available, and my own energy, time, and ambition. The output is how and what I decide to cook. I evaluate the success of a meal by how satisfied my family seems at the end of it, how much they’ve eaten, and how healthy the food was. Seeing how well it is received and how much of it is enjoyed allows me to update my model for the next time I cook. The updates and adjustments make it what statisticians call a “dynamic model.”
Over the years I’ve gotten pretty good at making meals for my family, I’m proud to say. But what if my husband and I go away for a week, and I want to explain my system to my mom so she can fill in for me? Or what if my friend who has kids wants to know my methods? That’s when I’d start to formalize my model, making it much more systematic and, in some sense, mathematical. And if I were feeling ambitious, I might put it into a computer program.
Ideally, the program would include all of the available food options, their nutritional value and cost, and a complete database of my family’s tastes: each individual’s preferences and aversions. It would be hard, though, to sit down and summon all that informationoff the top of my head. I’ve got loads of memories of people grabbing seconds of asparagus or avoiding the string beans. But they’re all mixed up and hard to formalize in a comprehensive list.
The better solution would be to train the model over time, entering data every day on what I’d bought and cooked and noting the responses of each family member. I would also include parameters, or constraints. I might limit the fruits and vegetables to what’s in season and dole out a certain amount of Pop-Tarts, but only enough to forestall an open rebellion. I also would add a number of rules. This one likes meat, this one likes bread and pasta, this one drinks lots of milk and insists on spreading Nutella on everything in sight.
If I made this work a major priority, over many months I might come up with a very good model. I would have turned the food management I keep in my head, my informal internal model, into a formal external one. In creating my model, I’d be extending my power and influence in the world. I’d be building an automated me that others can implement, even when I’m not around.
There would always be mistakes, however, because models are, by their very nature, simplifications. No model can include all of the real world’s complexity or the nuance of human communication. Inevitably, some important information gets left out. I might have neglected to inform my model that junk-food rules are relaxed on birthdays, or that raw carrots are more popular than the cooked variety.
To create a model, then, we make choices about what’s important enough to include, simplifying the world into a toy version that can be easily understood and from which we can infer important facts and actions. We expect it to handle only one job and accept that it will occasionally act like a clueless machine, one with enormous blind spots.
Sometimes these blind spots don’t matter. When we ask Google Maps for directions, it models the world as a series of roads, tunnels, and bridges. It ignores the buildings, because they aren’t relevant to the task. When avionics software guides an airplane, it models the wind, the speed of the plane, and the landing strip below, but not the streets, tunnels, buildings, and people.
A model’s blind spots reflect the judgments and priorities of its creators. While the choices in Google Maps and avionics software appear cut and dried, others are far more problematic. The value-added model in Washington, D.C., schools, to return to that example, evaluates teachers largely on the basis of students’ test scores, while ignoring how much the teachers engage the students, work on specific skills, deal with classroom management, or help students with personal and family problems. It’s overly simple, sacrificing accuracy and insight for efficiency. Yet from the administrators’ perspective it provides an effective tool to ferret out hundreds of apparently underperforming teachers, even at the risk of misreading some of them.
Here we see that models, despite their reputation for impartiality, reflect goals and ideology. When I removed the possibility of eating Pop-Tarts at every meal, I was imposing my ideology on the meals model. It’s something we do without a second thought. Our own values and desires influence our choices, from the data we choose to collect to the questions we ask. Models are opinions embedded in mathematics.
Whether or not a model works is also a matter of opinion. After all, a key component of every model, whether formal or informal, is its definition of success. This is an important point that we’ll return to as we explore the dark world of WMDs. In each case, we must ask not only who designed the model but also what that person or company is trying to accomplish. If the North Korean government built a model for my family’s meals, for example, it might be optimized to keep us above the threshold of starvation at the lowest cost, based on the food stock available. Preferences would count for little or nothing. By contrast, if my kids were creating the model, success might feature ice cream at every meal. My own model attempts to blend a bit of the North Koreans’ resource management with the happiness of my kids, along with my own priorities of health, convenience, diversity of experience, and sustainability. As a result, it’s much more complex. But it still reflects my own personal reality. And a model built for today will work a bit worse tomorrow. It will grow stale if it’s not constantly updated. Prices change, as do people’s preferences. A model built for a six-year-old won’t work for a teenager.
This is true of internal models as well. You can often see troubles when grandparents visit a grandchild they haven’t seen for a while. On their previous visit, they gathered data on what the child knows, what makes her laugh, and what TV show she likes and (unconsciously) created a model for relating to this particular four-year-old. Upon meeting her a year later, they can suffer a few awkward hours because their models are out of date. Thomas the Tank Engine, it turns out, is no longer cool. It takes some time to gather new data about the child and adjust their models.
This is not to say that good models cannot be primitive. Some very effective ones hinge on a single variable. The most common model for detecting fires in a home or office weighs only one strongly correlated variable, the presence of smoke. That’s usually enough. But modelers run into problems—or subject us to problems—when they focus models as simple as a smoke alarm on their fellow humans.
Racism, at the individual level, can be seen as a predictive model whirring away in billions of human minds around the world. It is built from faulty, incomplete, or generalized data. Whether it comes from experience or hearsay, the data indicates that certain types of people have behaved badly. That generates a binary prediction that all people of that race will behave that same way.
Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias. In this way, oddly enough, racism operates like many of the WMDs I’ll be describing in this book.
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Product details
- Publisher : Crown; 1st edition (September 6, 2016)
- Language : English
- Hardcover : 272 pages
- ISBN-10 : 0553418815
- ISBN-13 : 978-0553418811
- Item Weight : 12.8 ounces
- Dimensions : 5.79 x 1.09 x 8.54 inches
- Best Sellers Rank: #249,122 in Books (See Top 100 in Books)
- #52 in Privacy & Surveillance in Society
- #154 in Business Statistics
- #266 in Social Aspects of Technology
- Customer Reviews:
About the author

I am a mathematician turned quant turned algorithmic auditor living in Cambridge, MA.
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Unfortunately the logic in the book is a dumpster fire. I was astonished given that the author holds a PhD in mathematics... a very logical discipline.
The main thesis of the book is that there are certain conditions for an algorithm in which it can become a 'weapon of math destruction', and tries to show examples of these cases. O'Neil is decidedly anti-big data and anti-modeling in this book.
Here are my main complaints:
1. Her treatment of all of the examples is offensive to the experts who actually do social science in those fields. She clearly has only a surface knowledge of these issues, makes many factual errors, and does not actually know what current social scientists are working on. For example, in the section about policing, O'Neil says that if the Chicago Police Department hired her as their data scientist (!) she could make these biases and issues with the models go away, all while completely oblivious to what current economists, sociologists, and other experts are working on.
2. The claims made by O'Neil in this book are all testable hypotheses, however she makes NO effort to use data to make her argument, and instead relies on scant anecdotes and sweeping generalizations.
3. O'Neil was contradictory as to whether people are the problem or algorithms are the problem. For example, in the section about Starbucks and employee scheduling software she slammed the managers who took control over the algorithm, but then later explained that we don't have enough people actually being involved who adjust the algorithms as necessary... So which is it?
4. She misses the nuance between 'good' and 'bad' aspects of models. For example, when discussing the US News rating system for colleges, she argues that it isn't appropriate to rank schools. Then she goes on to attack for-profit colleges, while failing to acknowledge that the US News rating system can help guide someone who is underprivileged and doesn't have college counselors to tell them that the for-profit colleges are terribly terribly ranked.
5. She needs to look up the word 'arbitrary' in the dictionary. I'll quote the definition here: "based on random choice or personal whim, rather than any reason or system". Many times throughout the book she describes the choices of models in her examples as 'arbitrary'. A model is the exact OPPOSITE of arbitrary. It makes choices based on the defined rules of the program...
6. There is no original content or analysis in this book, beyond her coining of the phrase of 'weapons of math destruction'.
7. I'm confused why people say the book is well written. It isn't. It rambles and often strays away from the thesis.
In short, she does a disservice to the nuance involved with data and algorithms. She identifies some of the important issues near the beginning (e.g. sample size, out-of-sample conclusions, poor objective functions), however, her poor understanding of her examples, and hack-job of an argument is unfortunate and ultimately damning.
As a research scientist in the field of analytics I support most all her research recommendations. The technology implementing analytic models (e.g. predictive analytics and AI) is often leaving the supporting science behind. Mastering the science behind, rather than just advancing, these analytic technologies is as important as it is currently unpopular for research investment. But, if these inquiries into “WMD” are to be defensible research with persuasive results they cannot be colored by social and logical biases like Ms. O’Neil’s personal definition of morality or what a utopian society would look like.
Cathy O’Neil has a PhD in math from Harvard, taught at Barnard, decided to make three times the money by working as a “quant” on Wall Street, specifically for the hedge fund D. E. Shaw. Of the numerous wry observations she makes in the book, she compares working at D.E. Shaw to the structure of Al Qaeda. Information was tightly controlled in individual “cells.” No one (probably even the big boys) understood the entire structure which prevented someone “walking” to a rival. The financial meltdown of 2008, when suddenly the quants, and others, realized that a strawberry picker named Alberto Ramirez, making $14,000 a year, really couldn’t afford the $720,000 he financed in Rancho Grande, CA,, and therefore the “Triple A” rating on the bonds issued based on the mortgage was phony, proved to be her “Saul on the road to Damascus moment,” which eventually led to this book. (She doesn’t make the point that the damage done by the quants, in terms of lost homes and jobs, to so many Americans, was far, far greater than Al Qaeda’s wildest aspirations.)
In her book, O’Neil goes far beyond Wall Street to other segments of our society: colleges, the judicial system, insurance, advertising, employment, teacher evaluations, credit scores, and political campaigns and Facebook.
Consider colleges. It was US News and World Report that dreamed up the idea of ranking colleges based on “objective” quantitative criteria. They convinced others to play along, in particular the colleges themselves. And so, from the perspective of a university President, “…they were at the summit of their careers dedicating enormous energy toward boosting performance in fifteen areas defined by a group of journalists at a second-tier news magazine.” A most important area was totally omitted: “value for money,” a standard criteria for most Amazon Vine reviews. And so, as she says, to meet these journalists’ criteria, the cost of higher education rose 500% between 1985 and 2013. She cites a couple of examples how colleges “gamed” the system. The most interesting was King Abdulaziz University in Saudi Arabia. Its math department had been around TWO years, in 2014, when it came in 7th place in the world, behind Harvard, but ahead of MIT and Cambridge! How? It searched the professional journals for professors with the most citations, one of the criteria in the algorithm, offered the professors $72,000 a year for three weeks of work as “adjunct faculty.” Voila.
In public school teacher evaluations in the USA, O’Neil cites the example of a well-respected teacher who was fired for being in the bottom 10% in teacher evaluations. How? Apparently the teachers from the PREVIOUS year had falsified the students’ standardized testing results. The following year, when the well-respected teacher did not, it appeared to the algorithm that the students had declined. No appeal or common sense. She was fired.
Insurance is a personal bugaboo with me. O’Neil confirmed what I learned the hard way. A MAJOR factor in determining the price of insurance is an algorithm that determines which customers are unlikely to switch insurance companies – and those customers are charged the most! When I finally figured this out, the hard way, a few years back, the company that famously proclaims that you can “save 15% or more” was actually willing to drop my insurance premium 30% because I was changing, which I still did, to another company that offered the same coverage for 50% less. (I’ll be changing from that company in a couple of years, of course.) (What a racket.)
Another fascinating section is on how our on-line behavior is monitored, which changes not only the ads we see, but the very news. And how much effort is expended in political campaigns on those few undecided voters in Florida and Ohio. Wow. Truly calls for the abolition of the Electoral College.
Finally, my own example. I once worked for the COO of the most famous hospital in the aforementioned Saudi Arabia. He called me in one day and asked if I could do standard deviations. Thanks to Bill Gates, et al., I assured him I could readily do them. “Then please do them on all the doctors’ salaries, per department”. Again, thanks to Bill, it was done in a day. Why, oh why? It was the COO’s own “algorithm.” When he met monthly with each department Chair, to discuss physician evaluations and salary increases, there would be nothing “personal” involved. He could point to this objective report, and express his concerns about the “standard deviation” of the salaries within the department. And depending on – hum – the circumstances, he could say: I think the standard deviation is “too high” (or, of course, “too low”). The “basis” for giving out a 2 ½% or 5% salary increase. “Clever.”
As for O’Neil’s book, 5-stars, plus.
Top reviews from other countries



If you are looking for a really in depth text, this is not it. This is meant for everyone and in that respect it is a good book. When I started reading it I thought perhaps it could do with the Michael Lewis touch (Flashboys, Moneyball etc) but when I finished I thought not. It looks like this is the way the future looks, and it is not pretty the way it is right now. Let's hope that the technology and scale leads to greater beneifts to all, and not mass categorisation with a dumb central machine.

The author passion for the subject can be felt through the book and although enjoyed it did leave me feeling a little saddened to see so many examples of poorly implemented analytics.
The book would have benefitted from a broader perspective and also included success stories to show how data can also be a force for good to simplify and improve our lives. At the end of the day these are tools and it’s how people use them that can produce these WMD’s.
Also reminds us of the need to educate the wider community on how data is used and that data asset needs to benefit everyone.

It's tough luck being the 1 but it just means you will have to fight harder. It gives people the excuse to blame it on 'generalisation' and allows individuals to scream 'I am unique, you must take under account me as well!!', which in my honest opinion shows a very massive degree of selfishness and narcissism. People are amazing because we adapt and find identity in this adaptation but also respect.
Book is well written though and does have examples. It just doesn't evoke positivity in me.