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Cathy O'Neil

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Paperback – September 5, 2017

by Cathy O'Neil (Author)

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NEW YORK TIMES BESTSELLER • A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric—with a new afterword

“A manual for the twenty-first-century citizen . . . relevant and urgent.”—Financial Times

NATIONAL BOOK AWARD LONGLIST • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review • The Boston Globe • Wired • Fortune • Kirkus Reviews • The Guardian • Nature • On Point

We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.

But as mathematician and data scientist Cathy O’Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination—propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data.

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Crown
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September 5, 2017
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5.17 x 0.61 x 7.92 inches
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0553418831
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978-0553418835
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The privileged, we’ll see time and again, are processed more by people, the masses by machines.
Highlighted by 3,237 Kindle readers
So to sum up, these are the three elements of a WMD: Opacity, Scale, and Damage.
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A model’s blind spots reflect the judgments and priorities of its creators.
Highlighted by 2,703 Kindle readers

Editorial Reviews

Review

“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 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

“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

About the Author

Cathy O'Neil is a data scientist and author of the blog mathbabe.org. She earned a Ph.D. in mathematics from Harvard and taught at Barnard College before moving to the private sector, where she worked for the hedge fund D. E. Shaw. She then worked as a data scientist at various start-ups, building models that predict people’s purchases and clicks. O’Neil started the Lede Program in Data Journalism at Columbia and is the author of Doing Data Science. She is currently a columnist for Bloomberg View.

Excerpt. © Reprinted by permission. All rights reserved.

1

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.

Product details

Publisher ‏ : ‎ Crown; Reprint edition (September 5, 2017)
Language ‏ : ‎ English
Paperback ‏ : ‎ 288 pages
ISBN-10 ‏ : ‎ 0553418831
ISBN-13 ‏ : ‎ 978-0553418835
Item Weight ‏ : ‎ 7.2 ounces
Dimensions ‏ : ‎ 5.17 x 0.61 x 7.92 inches

Best Sellers Rank: #9,474 in Books (See Top 100 in Books)
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David Zetland

Very clear, but over-reliant on government solutions instead of more choices for consumers (competition!)

Reviewed in the United States 🇺🇸 on February 8, 2017

Verified Purchase

I was excited to read this book as soon as I heard Cathy O'Neill, the author, interviewed on EconTalk.

O'Neill's hypothesis is that algorithms and machine learning can be useful, but they can also be destructive if they are (1) opaque, (2) scalable and (3) damaging. Put differently, an algorithm that determines whether you should be hired or fired, given a loan or able to retire on your savings is a WMD if it is opaque to users, "beneficiaries" and the public, has an impact on a large group of people at once, and "makes decisions" that have large social, financial or legal impacts. WMDs can leave thousands in jail or bankrupt pensions, often without warning or remorse.

As examples of non-WMDs, consider bitcoin/blockchain (the code and transactions are published), algorithms developed by a teacher (small scale), and Amazon's "recommended" lists, which are not damaging (because customers can decide to buy or not).

As examples of WMDs (many of which are explained in the book), consider Facebook's "newsfeed" algorithm, which is opaque (based on their internal advertising model), scaled (1.9 billion disenfranchised zombies) and damaging (echo-chamber, anyone?)

I took numerous notes while reading this book, which I think everyone interested in the rising power of "big data" (or big brother) or bureaucratic processes should read, but I will only highlight a few:

* Models are imperfect -- and dangerous if they are given too much "authority" (as I've said)
* Good systems use feedback to improve in transparent ways (they are anti-WMDs)
WMDs punish the poor because the rich can afford "custom" systems that are additionally mediated by professionals (lawyers, accountants, teachers)
* Models are more dangerous the more removed their data are from the topic of interest, e.g., models of "teacher effectiveness" based on "student grades" (or worse alumni salaries)
* "Models are opinions embedded in mathematics" (what I said) which means that those weak in math will suffer more. That matters when "American adults... are literally the worst [at solving digital problems] in the developed world."
* It is easy for a "neutral" variable (e.g., postal code) to reproduce a biased variable (e.g., race)
* Wall Street is excellent at scaling up a bad idea, leading to huge financial losses (and taxpayer bailouts). It was not an accident that Wall Street "messed up." They knew that profits were private but losses social.
* Many for-profit colleges use online advertisements to attract (and rip off) the most vulnerable -- leaving them in debt and/or taxpayers with the bill. Sad.
* A good program (for education or crime prevention) also relies on qualitative factors that are hard to code into algorithms. Ignore those and you're likely to get a biased WMD. I just saw a documentary on urbanism that asked "what do the poor want -- hot water or a bathtub?" They wanted a bathtub because they had never had one and could not afford to heat water. #checkyourbias
* At some points in this book, I disagreed with O'Neill's preference for justice over efficiency. She does not want to allow employers to look at job applicants' credit histories because "hardworking people might lose jobs." Yes, that's true, but I can see why employers are willing to lose a few good people to avoid a lot of bad people, especially if they have lots of remaining (good credit) applicants. Should this happen at the government level? Perhaps not, but I don't see why a hotel chain cannot do this: the scale is too small to be a WMD.
* I did, OTOH, notice that peer-to-peer lending might be biased against lender like me (I use Lending Club, which sucks) who rely on their "public credit models" as it seems that these models are badly calibrated, leaving retail suckers like me to lose money while institutional borrowers are given preferential access.
* O'Neill's worries about injustice go a little too far in her counterexamples of the "safe driver who needs to drive through a dangerous neighborhood at 2am" as not deserving to face higher insurance prices, etc. I agree that this person may deserve a break, but the solution to this "unfair pricing" is not a ban on such price discrimination but an increase in competition, which has a way of separating safe and unsafe drivers (it's called a "separating equilibrium" in economics). Her fear of injustice makes me think that she's perhaps missing the point. High driving insurance rates are not a blow against human rights, even if they capture an imperfect measure of risk, because driving itself is not a human right. Yes, I know it's tough to live without a car in many parts of the US, but people suffering in those circumstances need to think bigger about maybe moving to a better place.
* Worried about bias in advertisements? Just ban all of them.
* O'Neill occasionally makes some false claims, e.g., that US employers offered health insurance as a perk to attract scarce workers during WWII. That was mainly because of a government-ordered wage freeze that incentivised firms to offer "more money" via perks. In any case, it would be good to look at how other countries run their health systems (I love the Dutch system) before blaming all US failures on WMDs.
* I'm sympathetic to the lies and distortions that Facebook and other social media spread (with the help of WMDs), but I've gotta give Trump credit for blowing up all the careful attempts to corral, control and manipulate what people see or think (but maybe he had a better way to manipulate). Trump has shown that people are willing to ignore facts to the point where it might take a real WMD blowing up in their neighborhood to take them off auto pilot.
* When it comes to political manipulations, I worry less about WMDs than the total lack of competition due to gerrymandering. In the 2016 election, 97 percent of representatives were re-elected to the House.
* Yes, I agree that humans are better at finding and using nuances, but those will be overshadowed as long as there's a profit (or election) to win. * * * Can we push back on those problems? Yes, if we realize how our phones are tracking us, how GPA is not your career, or how "the old boys network" actually produced a useful mix of perspectives.
* Businesses will be especially quick to temper their enthusiasm when they notice that WMDs are not nearly so clever. What worries me more are politicians or bureaucrats who believe a salesman pitching a WMD that will save them time but harm citizens. That's how we got dumb do not fly lists, and other assorted government failures.
* Although I do not put as much faith in "government regulation" as a solution to this problem as I put into competition, I agree with O'Neill that consumers should own their data and companies only get access to it on an opt-in model, but that model will be broken for as long as the EULA requires that you give up lots of data in exchange for access to the "free" platform. Yes, Facebook is handy, but do you want Facebook listening to your phone all the time?

Bottom Line: I give this book FOUR STARS for its well written, enlightening expose of MWDs. I would have preferred less emphasis on bureaucratic solutions and more on market, competition, and property rights solutions.

16 people found this helpful

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A catchy title…

Reviewed in the United States 🇺🇸 on May 15, 2020

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…and a book that delivers by delving into the numerous ways that mathematical algorithms impact our lives, sometimes very negatively and often without appeal. All too often the ones who formulate the algorithms view the injustices as just so much collateral damage, sacrificed on the altar of economic efficiency. At the end of the review, I’ll provide an example of my own.

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.

10 people found this helpful

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Richard2047

Rather superficial view of how algorithms affect us

Reviewed in the United Kingdom 🇬🇧 on February 12, 2021

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A pop science book about algorithms. US bias in the language distracts from the message - apart from 'math' instead of 'maths' for 'mathematics', words like 'maven', 'honor roll' and 'Super PACS' (which should be 'PACs') have little relevance outside the US. Some basic errors in research, too, such as reference to "a quality computer, like the IBM Selectric" (page 115) and the reference to "the British city of Kent" (page 89) suggest a lack of deep knowledge of the subject, and the description of machine learning (pages 75-77) is superficial, failing to explain the distinction between data mining, machine learning and artifical intelligence in general. Plenty of anecdotes but not much science, some of it wrong. I was surprised to read "BMI is a person's weight in kilograms divided by their height in centimetres" (page 176). By that measure, a 6'6" man (200 cm) could weight 5 tons (5000 kg) and be 'normal' with a BMI of 25. No wonder so many people are overweight if that's the American way. Of course, BMI is actually weight (in kg) divided by the square of height (in metres). Curiously, until the NIH redefined U.S. BMI cut-offs in 1998, LeBron James (I had to look him up) with a BMI of 26.6 would be 'normal'. Errors like that cast doubt on the rest of the book, and I wonder what else I missed.

22 people found this helpful

PhilipM

American - not for Europeans

Reviewed in the United Kingdom 🇬🇧 on September 5, 2017

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Interesting but very American based and many examples were meaningless to me in Britain.

46 people found this helpful

Ace

A important warning on the dark side of data. Join the data movement and use it as a force for good

Reviewed in the United Kingdom 🇬🇧 on May 7, 2018

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This is an easy to read book with a raft of real world examples across a range of industries from education, finance to ecommerse on where data has been mis-used and negatively impacted customers and employees. It’s an important perspective on how data can be misused and offers ideas on how we need to adapt our control & culture in an increasing data driven world.
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.

11 people found this helpful

n1msr

Interesting message - the future is here

Reviewed in the United Kingdom 🇬🇧 on December 30, 2019

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WMD is a well-known book by now, and I have been meanin to read it for some time. It is short and easy to read. Plenty of examples from different industries, starting with finance. Cathy's experience in this and other Big Data and Data Scientist roles helps to give her message credance. She explains the examples well.

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.

4 people found this helpful

Amazon Customer

High hopes for the book - disappointed

Reviewed in the United Kingdom 🇬🇧 on June 7, 2019

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It's a bit too much of a cheer for the sidekick if you will. Granted there are people that cannot be put in a category, but saying that models are ridiculous because they don't accomodate for the 1 in a 10000 is a bit much. Statistical significance people! I read until the end of the first case ( teacher got fired because of poorly performing kids).

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.

4 people found this helpful

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