Weapons of Math Destruction

Weapons of Math Destruction

How Big Data Increases Inequality and Threatens Democracy

eBook - 2016
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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 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.
Publisher: Crown


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Aug 29, 2020

Should be required reading for all high school students. Too important to ignore.

Nov 08, 2018

Read for Strangr Than Fiction book discussion group. Join us for the discussion at the Plaza Branch on Tuesday 11/13/2018

Oct 17, 2018

A very good read, although I do wish she had listed more studies to support her findings. Definitely one more thing that big data is doing to undermine the little guy

Sep 22, 2018

Unbalanced. Although I did find the book interesting, the author pretty much rants for 300 pages. Her theories are supported by isolated anecdotes. She invariably points out whenever WMD models have miserably failed and caused much harm. However, she rarely supports with credible documentation how such harm was done. She made no effort to make it a balanced investigation.

My sense was that there is another side to this WMD story. To investigate the other side, I read all the “1” rated reviews that were insightful. Some of the reviewers had studied several of the WMD models she criticized. And, these WMD models were invariably more transparent, had been subject to more scrutiny, and had been dynamically upgraded over time to overcome their original flaws than as she depicted.

What the author did is tell us what we wanted to hear. Big Data is unfair, biased, harmful, etc. We have to regulate it, render it more transparent, etc. However, the way she went about it may hurt the cause she is advocating.

Sep 12, 2018

Big Data, Algorithms, and biased stats. If you're a minority or part of the disenfranchised this book reveals what we've known for decades but intensified with web 2.0. Data mining and with Facebook being under fire as well as Amazon for collecting every information about us, selling it and targeting us as consumers and keeping us in the ruts or routines of our lives. A very interesting read.

Dec 16, 2017

Updated review: There are some pretty good books on the nefarious algorithms out there and their corporate owners, but this is one of the more mediocre ones.
I recently heard an annoying talk by this author in Seattle. Some of the comments here made me wonder if I had been too harsh on this author, but her fallacious absolutes so blithely tossed out there in her talk and book; her outlandish statement about creating // risk-free \\ credit default swaps [no such thing as a risk-free insurance swindle] convinced me my review was far too charitable!
One of her many absolutes which particularly annoying was her absolutist statement that poor people do poorly on standardized tests, while rich people excel on them - - conveniently completely ignoring [avoiding ???] how testing is used as an insidious economic weapon.
Having come from the lowest economic level, and working and studying industriously, such absolutes I find particularly trivializing.
I recall while working overseas in Hong Kong coming into contact with an elderly flower lady [who sold flowers day and night in various restaurants and bars, et cetera] who was putting her daughter through Harvard. Many poor immigrants routinely excel at standardized tests - which negates this author's specious remarks.
Within corporations, testing is used [and universally across the planet] to rig the hiring and promotion process - - always disbelieve any situation where the management refuses to divulge your test results - - they are lying their butts off!
Yes, I was forced to walk miles in freezing winter weather to the library to use encyclopedias and dictionaries and other references, while richer kids might have them at their own homes for easy access, saving valuable time and energy, et cetera, so the poor invariably have to work far harder, but please never negate or trivialize them!
[One frequently reads of studies stating that one's superior academic record has little correlation with later financial success in life - - without mentioning companion studies, verified again and again, explaining that the chief indicator of one's financial success in life is the family the individual is born into!]

Jul 31, 2017

The author is cogent, extremely engaging and writes in a way that draws you in and makes continuing on effortless...as she educates you about what is going on and gives real life examples of how critical things get delegated to computers and algorithms -- but often have unintended consequences -- that are unjust and or cause very real harms to real people. But as any of you know -- actually finding and being able to engage a REAL person at some company or government agency can be difficult -- and then when you finally do, the odds are -- they don't have the power or ability to do anything about it.

Whether its about credit, health care, the Affordable Care Act, being hired for a job, increasingly rigid algorithms based upon, "proxy" (something picked to approximate something that might be hard to assess or measure --- and therefore WRONG!) are determining outcomes -- and we never know, let alone have any ability to challenge these decisions. What's worse -- is the errors serve to reinforce and exacerbate already existing injustices in our society (like race and other forms of systemic privilege)

They used to say, "You can't fight city hall..." But that was child's play compared to trying to fight an algorithm -- even worse, most of the time, you will never know that your bad fortune was in fact CAUSED by an unjust algorithm.

Read this book -- along with, "The New Jim Crow" and you will really have a much better understanding of the challenges that if not addressed threaten to unravel quality of life in the US for everyone.

Jul 23, 2017

This is an interesting read for someone in his/her seventh decade (i.e. me) and a must read for someone in their third, fourth and fifth decade. It’s always important to understand what you are up against. (There is likely an algorithm monitoring these comments.)

JCLChrisK May 26, 2017

You are prey. The predator is numbers. Numbers that have been carefully designed to turn you into prey. Numbers wielded by marketers, politicians, insurance companies, and so many others. The problem with these particular numbers is that they give those using them the illusion of knowing you when all they really manage is a proxy, a mathematical approximation that may or may not be accurate. And they are built into self-feeding, self-affirming, reinforcing loops that make them ever more restrictive and controlling. They don't simply feed on us, they increasingly define us.

Cathy O'Neil has been a mathematics professor and has worked in the data science industry in a variety of businesses and roles. She knows how the numbers work and has seen them in action from multiple perspectives. At the start of her conclusion in Weapons of Math Destruction, she writes:

"In this march through a virtual lifetime, we’ve visited school and college, the courts and the workplace, even the voting booth. Along the way, we’ve witnessed the destruction caused by WMDs. Promising efficiency and fairness, they distort higher education, drive up debt, spur mass incarceration, pummel the poor at nearly every juncture, and undermine democracy. It might seem like the logical response is to disarm these weapons, one by one.

"The problem is that they’re feeding on each other. Poor people are more likely to have bad credit and live in high-crime neighborhoods, surrounded by other poor people. Once the dark universe of WMDs digests that data, it showers them with predatory ads for subprime loans or for-profit schools. It sends more police to arrest them, and when they’re convicted it sentences them to longer terms. This data feeds into other WMDs, which score the same people as high risks or easy targets and proceed to block them from jobs, while jacking up their rates for mortgages, car loans, and every kind of insurance imaginable. This drives their credit rating down further, creating nothing less than a death spiral of modeling. Being poor in a world of WMDs is getting more and more dangerous and expensive.

"The same WMDs that abuse the poor also place the comfortable classes of society in their own marketing silos. . . . The quiet and personal nature of this targeting keeps society’s winners from seeing how the very same models are destroying lives, sometimes just a few blocks away."

O'Neil has crafted a broad overview that introduces the complexity of the topic with numerous examples, and through it a call to wield those tools more ethically and morally. The book is highly accessible, intelligent without being difficult and entertaining without being frivolous. This is a book that deserves high readership and a topic that needs extensive discussion.

"Predictive models are, increasingly, the tools we will be relying on the run our institutions, deploy our resources, and manage our lives. But as I’ve tried to show throughout this book, these models are constructed not just from data but from the choices we make about which data to pay attention to—and which to leave out. Those choices are not just about logistics, profits, and efficiency. They are fundamentally moral."

Feb 20, 2017

Even if one has some amount of math skills -- let's say some algebra, geometry, and perhaps a bit of calculus -- a maths doctorate will try to shut down any arguments by resorting to esoteric mathematical concepts: "You don't know this abstruse aspect of maths, therefore you're not qualified to express doubts about the objectivity of our algorithms."

How informative, then, to have a Harvard PhD in maths with both academic and business experience debunk the self-proclaimed objectivity of mathematics. Among other revelations, Dr. O'Neil points out the insidious nature of self-reinforcing applications of "objective" mathematics that end up portraying a very distorted version of events.

Definitely a must-read for those who perceive mathematically minded technologists supplanting theologians as self-proclaimed holders of The One and Only Truth...

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JCLChrisK May 26, 2017

Models, despite their reputation for impartiality, reflect goals and ideology. . . . 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.

JCLChrisK May 26, 2017

Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide. We have to explicitly embed better values into our algorithms, creating Big Data models that follow our ethical lead. Sometimes that will mean putting fairness ahead of profit.

JCLChrisK May 26, 2017

We’ve seen time and again that mathematical models can sift through data to locate people who are likely to face great challenges, whether from crime, poverty, or educations. It’s up to society whether to use that intelligence to reject and punish them—or to reach out to them with the resources they need. We can use the scale and efficiency that make WMDs so pernicious in order to help people. It all depends on the objective we choose.

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