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Weapons of Math Destruction: How Big Data…
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Weapons of Math Destruction: How Big Data Increases Inequality and… (2016)

by Cathy O'Neil

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Showing 1-5 of 39 (next | show all)
4.5/5
This book is very important. ( )
  Spr1t3 | Jul 31, 2018 |
Quick read; clear descriptions of a difficult topic. Be amazed and enraged at inequality created and exacerbated by the opaque closed systems that affect every part of our lives. ( )
  Quasifesto | Jan 4, 2018 |
"Weapons of Math Destruction" lays out a number of clear and creepy cases of how predictive algorithms get things wrong, with negative and, in some cases, infuriating consequences. O'Neil walks readers through anecdotes from the worlds of education, business, law enforcement, insurance, politics, and, of course, social media. She illustrates that time and again, the formulae designed to describe us become their own self-fulfilling prophecies, often in ways that marginalise the already disenfranchised. The book is well-written and a much-needed wake-up call as we cede more and more of our personal data both with and without knowing it to entities that use it in ways they never clearly tell us about. O'Neil writes with an obvious liberal bias, and I was disappointed that her solution was to look for ways to improve big data rather than retreating from our over-reliance on it, but otherwise, many of her conclusions are hard to argue with. At the same time, she teaches her readers that even the so-called "science" that seems so irrefutable is always worth a second glance. ( )
  quaintlittlehead | Jan 1, 2018 |
Summary: An insider account of the algorithms that affect our lives, from going to college, to the ads we see online, to our chances of getting a job, being arrested, getting credit and insurance.

Big Data is indeed BIG. Mathematical algorithms shape who will see this post on their Facebook newsfeed. If you go to Amazon or another online bookseller, algorithms will suggest other books like this one you might be interested in. Have you seen all those ads about credit scores? They are more important than you might imagine. Algorithms used by employers and insurance companies determine your employability and insurability in part through these scores. Far more than another credit card (bad idea, by the way) or a mortgage are on the line. These algorithms seem objective, but how they are formulated, and the assumptions made in doing so mean the difference between useful tools that benefit people, and "black boxes" that thwart the flourishing of others, often unknown to them.

Cathy O'Neil should know. A tenure track math professor, she made the jump to Wall Street and became a "quant" who helped develop mathematical algorithms and witnessed, in the crash of 2008, the harm some of these caused. And she began to notice how algorithms often painfully impacted the lives of many others. She describes how a teacher was fired because of the weighting of performance scores of a single class, despite other evaluations finding her an excellent teacher (afterwards it was found that there were a high number of erasures on tests for students who would have been in her class the previous year, suggesting these had been altered to improve scores).

As she looked at the algorithms responsible for such injustices, she came to dub them "Weapons of Math Destruction" or WMDs and she identified three characteristics of these WMDs:

Opacity: those whose lives are affected by them have no idea of the factors and weighting of those factors that contributed to their "score".

Scale: how widely an algorithm is applied across industries and sectors of life can affect how much of one's life is touched by a single formula. For example, the FICO scores mentioned above affect not only credit, but the ability to get a job, the cost of auto insurance, and your ability to rent an apartment.

Damage: WMDs can reinforce other factors perpetuating a cycle of poverty, or incarceration.

She also shows that what makes these algorithms destructive is the use of proxy measurements. For example an employer may not know directly how savvy someone is as a marketer, and so they use a "proxy" measurement of how many Twitter followers that person has. Or age is used as a proxy for how safe a driver one is. For a group, the proxy may work well, and be utterly inaccurate for an individual that falls within that proxy group.

Then in successive chapters, she chronicles some of the ways WMDs operate in different parts of life. She discusses the U.S. News & World Report college rankings, and the use of algorithms in admissions processes. Social media uses algorithms to target advertising, which means some will see ads for for-profit schools and payday lenders, and others for upscale furnishing or Viagra, based on clicks, likes, searches, and comments. Policing strategies, including locations for intensified "stop and frisk" policing are shaped by another set of algorithms. Algorithms to filter resumes may use scoring algorithms that discriminate by address and psych exam algorithms may render others unemployable in a certain industry. Scheduling algorithms may promote efficiency at the expense of the ability of workers to sleep on a regular schedule, or arrange childcare, or work enough hours to qualify for health insurance. Algorithms sometimes shut people out from credit or low cost insurance when in fact they are good risks. She concludes by showing how algorithms determine ads and news we see (and don't see). In an afterword she explores the flaws in algorithms revealed on the election of Donald Trump (algorithms, for example predicted Clinton would easily win Michigan and Wisconsin, where consequently she did not campaign, and lost by small margins).

In her conclusion, she makes the case not only for a code of ethics for mathematicians but also argues that regulation and audits of these algorithms are necessary. The value assumptions, as well as the mathematical methods of many algorithms are flawed, and yet opacity means those whose lives are most affected don't even know what hit them.

She helps us see both the sinister and useful side of these algorithms. They may reveal where a pro-active intervention may save a family from descending into family violence, or provide extra assistance to a child in danger of falling behind in a key subject. Or they may be used to invade personal rights, or even to perpetuate socio-economic divides in a society. The reality is that the problem is not the math but the old GIGO problem (garbage in, garbage out). The values and assumptions of the humans who devise the formulas and weightings of values and the use of proxies determine what may be destructive outcomes for some people. Yet it can be hidden behind an app, a program, an algorithm.

The massive explosion in storage capacities, processing speeds, and the way our interests, health status, travel patterns, spending patterns, fitness, diet and sleep habits, our political inclinations and more may be tracked via our online and smartphone usage makes O'Neil's warning an urgent one. We create mountains of data that may be increasingly mined by government and private interests. Perhaps as important as asking whether this will be governed in ways that contribute to our flourishing, is whether we will be alert enough to care.

____________________________

Disclosure of Material Connection: I received this book free from the publisher. I was not required to write a positive review. The opinions I have expressed are my own. ( )
1 vote BobonBooks | Oct 4, 2017 |
Weapons of Math Destruction, How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil. The over the top subtitle fairly well summarizes what the premise of the book is. WMD’s are mathematical models or algorithms that are used predict human behavior, and aide in decision making. In benign situations these models determine who you are matched with on dating sites, what movies Netflix's suggest you see, the products Amazon offers you and things of that ilk. In less than benign situations these models come up with the probabilities that suggest which person maybe a bad hire, a risky borrower, or a potential terrorist. These models determine if you receive advertisements from a payday loan service, a mutual fund company or determine how you are sentenced in criminal cases. O’Neil argues that the continuous use of these programs can create loops that keep the poor, poor and allow the rich to get richer. These models lack transparency, the results tend to unquestioned, and the creators are unaccountable for their actions. O’Neil is unapologetically biased against the widespread use of big data and WMD’s. The book focuses on the damage done by these models and social injustice they wreak on the populous. O’Neil’s does a good job in support of her premise. She uses over 200 citations in addition to extensive footnotes and the book has a good index. O’Neil is highly qualified author in this field. She has a Ph.D in mathematics from Harvard and is considered an authority in the field. She is a good writer, although each chapter follows a similar pattern. Each chapter covers a specific area of concern covering topics from college admissions, hiring practices, loan approval, and online advertising. She then demonstrates the harmful effects of big data and WMD’s. Politically she believes, “Successful micro-targeting, in part, explains why in 2015 more than 43 percent of Republicans ---still believe the that President Obama is a Muslin and that 20 percent believe he was born outside the United States”. Her solution to this issue involves more government oversight, greater transparency in the industry, and European style individual control of their data.
( )
  Cataloger623 | Sep 22, 2017 |
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Amazon.com Product Description (ISBN 0553418815, Hardcover)

A former Wall Street quant sounds an alarm on mathematical modeling—a pervasive new force in society that threatens to undermine democracy and widen inequality.
 
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 shocking 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 race or neighborhood), 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, from college to retirement, O’Neil exposes the black box models that shape our future, both as individuals and as a society. Models that score teachers and students, sort resumes, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health—all have pernicious feedback loops. They don’t simply describe reality, as proponents claim, they change reality, by expanding or limiting the opportunities people have. O’Neil calls on modelers to take more responsibility for how their algorithms are being used. 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.

(retrieved from Amazon Fri, 15 Apr 2016 20:07:53 -0400)

"A former Wall Street quantitative analyst sounds an alarm on mathematical modeling, a pervasive new force in society that threatens to undermine democracy and widen inequality,"--NoveList.

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