Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Hardcover – September 6, 2016
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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.”
“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.”
"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.”
“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.”
“A nuanced reminder that big data is only as good as the people wielding it.”
“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.”
“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.”
“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.”
“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.”
“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.”
“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.”
“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.”
“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.”
"[O'Neil's] propulsive study reveals many models that are currently 'micromanaging' the US economy as opaque and riddled with bias."
“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.”
"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.”
“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.”
"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."
“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 of Hand 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
- Item Weight : 12.8 ounces
- Hardcover : 272 pages
- ISBN-10 : 0553418815
- ISBN-13 : 978-0553418811
- Dimensions : 5.79 x 1.09 x 8.54 inches
- Publisher : Crown; 1st edition (September 6, 2016)
- Language: : English
- Best Sellers Rank: #151,176 in Books (See Top 100 in Books)
- Customer Reviews:
Top reviews from the United States
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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.
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This section is Amazon's way of using what it knows -- which book you're looking at, and sales data collected across all its customers -- to recommend other books that you might be interested in. It's a very simple, and successful, example of a predictive model: data goes in, some computation happens, a prediction comes out. What makes this a good model? Here are a few things:
1. It uses relevant input data.The goal is to get people to buy books, and the input to the model is what books people buy. You can't expect to get much more relevant than that.
2. It's transparent. You know exactly why the site is showing you these particular books, and if the system recommends a book you didn't expect, you have a pretty good idea why. That means you can make an informed decision about whether or not to trust the recommendation.
3. There's a clear measure of success and an embedded feedback mechanism. Amazon wants to sell books. The model succeeds if people click on the books they're shown, and, ultimately, if they buy more books, both of which are easy to measure. If clicks on or sales of related items go down, Amazon will know, and can investigate and adjust the model accordingly.
Weapons of Math Destruction reviews, in an accessible, non-technical way, what makes models effective -- or not. The emphasis, as you might guess from the title, is on models with problems. The book highlights many important ideas; here are just a few:
1. Models are more than just math. Take a look at Amazon's model above: while there are calculations (simple ones) embedded, it's people who decide what data to use, how to use it, and how to measure success. Math is not a final arbiter, but a tool to express, in a scalable (i.e., computable) way, the values that people explicitly decide to emphasize. Cathy says that "models are opinions expressed in mathematics" (or computer code). She highlights that when we evaluate teachers based on students' test scores, or assess someone's insurability as a driver based on their credit record, we are expressing opinions: that a successful teacher should boost test scores, or that responsible bill-payers are more likely to be responsible drivers.
2. Replacing what you really care about with what you can easily get your hands on can get you in trouble. In Amazon's recommendation model, we want to predict book sales, and we can use book sales as inputs; that's a good thing. But what if you can't directly measure what you're interested in? In the early 1980's, the magazine US News wanted to report on college quality. Unable to measure quality directly, the magazine built a model based on proxies, primarily outward markers of success, like selectivity and alumni giving. Predictably, college administrators, eager to boost their ratings, focused on these markers rather than on education quality itself. For example, to boost selectivity, they encouraged more students, even unqualified ones, to apply. This is an example of gaming the model.
3. Historical data is stuck in the past. Typically, predictive models use past history to predict future behavior. This can be problematic when part of the intention of the model is to break with the past. To take a very simple example, imagine that Cathy is about to publish a sequel to Weapons of Math Destruction. If Amazon uses only purchase data, the Customers Who Bought This Also Bought list would completely miss the connection between the original and the sequel. This means that if we don't want the future to look just like the past, our models need to use more than just history as inputs. A chapter about predictive models in hiring is largely devoted to this idea. A company may think that its past, subjective hiring system overlooks qualified candidates, but if it replaces the HR department with a model that sifts through resumes based only on the records of past hires, it may just be codifying (pun intended) past practice. A related idea is that, in this case, rather than adding objectivity, the model becomes a shield that hides discrimination. This takes us back to Models are more than just math and also leads to the next point:
4. Transparency matters! If a book you didn't expect shows up on The Customers Who Bought This Also Bought list, it's pretty easy for Amazon to check if it really belongs there. The model is pretty easy to understand and audit, which builds confidence and also decreases the likelihood that it gets used to obfuscate. An example of a very different story is the value added model for teachers, which evaluates teachers through their students' standardized test scores. Among its other drawbacks, this model is especially opaque in practice, both because of its complexity and because many implementations are built by outsiders. Models need to be openly assessed for effectiveness, and when teachers receive bad scores without knowing why, or when a single teacher's score fluctuates dramatically from year to year without explanation, it's hard to have any faith in the process.
5. Models don't just measure reality, but sometimes amplify it, or create their own. Put another way, models of human behavior create feedback loops, often becoming self-fulfilling prophecies. There are many examples of this in the book, especially focusing on how models can amplify economic inequality. To take one example, a company in the center of town might notice that workers with longer commutes tend to turn over more frequently, and adjust its hiring model to focus on job candidates who can afford to live in town. This makes it easier for wealthier candidates to find jobs than poorer ones, and perpetuates a cycle of inequality. There are many other examples: predictive policing, prison sentences based on recidivism, e-scores for credit. Cathy talks about a trade-off between efficiency and fairness, and, as you can again guess from the title, argues for fairness as an explicit value in modeling.
Weapons of Math Destruction is not a math book, and it is not investigative journalism. It is short -- you can read it in an afternoon -- and it doesn't have time or space for either detailed data analysis (there are no formulas or graphs) or complete histories of the models she considers. Instead, Cathy sketches out the models quickly, perhaps with an individual anecdote or two thrown in, so she can get to the main point -- getting people, especially non-technical people, used to questioning models. As more and more aspects of our lives fall under the purview of automated data analysis, that's a hugely important undertaking.
Top reviews from other countries
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.
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 book covers several different fields, all of them tied by the common theme of how the lack of understanding of algorithms, sometimes unintentionally, sometimes for personal gain, leads to entirely false evaluations of phenomena, based on spurious correlations or too simplistic approaches, that quite too often provide false bases on top of which important, but entirely unsubstantiated decisions, are already being made.
I strongly advise anyone, but in particular people working in IT, to read this book since it is important that more people are made aware of how little their choices, and who they really are, may matter in their lives, once their data is fed to the algorithms most companies use nowadays.
IT people can, and should, use this knowledge to push for a review of how people data is used to make decisions that affect people's lives.