Senin, 25 Juni 2012

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A Knight of the Seven Kingdoms (A Song of Ice and Fire), by George R. R. Martin

NEW YORK TIMES BESTSELLER • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY LOS ANGELES TIMES AND BUZZFEED • Taking place nearly a century before the events of A Game of Thrones, A Knight of the Seven Kingdoms compiles the first three official prequel novellas to George R. R. Martin’s ongoing masterwork, A Song of Ice and Fire. These never-before-collected adventures recount an age when the Targaryen line still holds the Iron Throne, and the memory of the last dragon has not yet passed from living consciousness.
 
Before Tyrion Lannister and Podrick Payne, there was Dunk and Egg. A young, naïve but ultimately courageous hedge knight, Ser Duncan the Tall towers above his rivals—in stature if not experience. Tagging along is his diminutive squire, a boy called Egg—whose true name is hidden from all he and Dunk encounter. Though more improbable heroes may not be found in all of Westeros, great destinies lay ahead for these two . . . as do powerful foes, royal intrigue, and outrageous exploits.
 
Featuring more than 160 all-new illustrations by Gary Gianni, A Knight of the Seven Kingdoms is a must-have collection that proves chivalry isn’t dead—yet.

Praise for A Knight of the Seven Kingdoms
 
“Readers who already love Martin and his ability to bring visceral human drama out of any story will be thrilled to find this trilogy brought together and injected with extra life.”—Booklist
 
“The real reason to check out this collection is that it’s simply great storytelling. Martin crafts a living, breathing world in a way few authors can. . . . [Gianni’s illustrations] really bring the events of the novellas to life in beautiful fashion.”—Tech Times

  • Sales Rank: #1944 in Books
  • Brand: Song of Ice and Fire, A
  • Published on: 2015-10-06
  • Released on: 2015-10-06
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.60" h x 1.20" w x 6.40" l, 1.37 pounds
  • Binding: Hardcover
  • 368 pages

From Publishers Weekly
The three interlocking novellas in this collection, all previously published, make for a rousing prelude to Martin's bestselling Song of Ice and Fire saga. Set 90 years before the events in A Game of Thrones, they chronicle the experiences of Ser Duncan the Tall, a humble hedge knight whose honorable comportment is often at odds with the schemes of the royals who rule the Seven Kingdoms. In "The Hedge Knight," Duncan is forced to fight a brutal trial by combat for defending a commoner against a cruel prince's son. In both "The Sworn Sword" and "The Mystery Knight," Duncan advances further toward fulfilling his destiny as a knight of the Kingsguard, foreseen in dreams throughout the stories. The stories are top-heavy with tournaments and bloody battles, but also rich in human drama and the colorful worldbuilding that distinguishes other books in the series. The appearance of youthful versions of characters who figure in the later novels makes this collection a must-read for Martin's legions of fans. (Oct.)\n

Review
“Readers who already love [George R. R.] Martin and his ability to bring visceral human drama out of any story will be thrilled to find this trilogy brought together and injected with extra life.”—Booklist
 
“The real reason to check out this collection is that it’s simply great storytelling. Martin crafts a living, breathing world in a way few authors can. . . . [Gary Gianni’s illustrations] really bring the events of the novellas to life in beautiful fashion.”—Tech Times

About the Author
George R. R. Martin is the #1 New York Times bestselling author of many novels, including the acclaimed series A Song of Ice and Fire—A Game of Thrones, A Clash of Kings, A Storm of Swords, A Feast for Crows, and A Dance with Dragons—as well as Tuf Voyaging, Fevre Dream, The Armageddon Rag, Dying of the Light, Windhaven (with Lisa Tuttle), and Dreamsongs Volumes I and II. He is also the creator of The Lands of Ice and Fire, a collection of maps from A Song of Ice and Fire featuring original artwork from illustrator and cartographer Jonathan Roberts, and The World of Ice & Fire (with Elio M. García, Jr., and Linda Antonsson). As a writer-producer, Martin has worked on The Twilight Zone, Beauty and the Beast, and various feature films and pilots that were never made. He lives with the lovely Parris in Santa Fe, New Mexico.
 
Gary Gianni graduated from the Chicago Academy of Fine Art in 1976. He has worked on numerous magazines, children’s books, graphic novels, and paperbacks. He has illustrated a variety of subjects including Batman, 20,000 Leagues Under the Sea, and Michael Chabon’s Gentlemen of the Road. He also drew the Prince Valiant comic strip from 2004 to 2012. Gianni is influenced by the masters of the golden age of illustrating, such as Joseph Clement Coll and Franklin Booth.

Most helpful customer reviews

107 of 114 people found the following review helpful.
A Satisfying Read
By The Invisible Pam
Somewhere in the 4-5 range. I really liked this book and can't wait for the next.

If you are expecting something like GAME OF THRONES you can go ahead and kill that expectation or put it on a shelf to languish. This book is delightfully different. In fact it reminds me of the historical classics like THE BLACK ARROW and IVANHOE. And if it wasn't for a handful of instances with naughty language the story would be suitable for 6th Graders on up. However it does use the C-word for lady bits and a few other choice words I can't recall at the moment, so it's not a book I'd hand to a youngster. (Editors, come out with a kid's version! You'll make more money)

When I first began reading I wasn't sure what I thought but it wasn't long until I got caught up in Dunk and Egg's meanderings through the kingdoms. Dunk is a huge young man that is as close to an honest, chivalrous knight as you can come. Egg is a prince in hiding and then a prince in training as a Dunk's squire.

As Dunk is a 'Hedge Knight', which is to say he freelances, they meander from job to job taking in the occasional tourney. They, of course, run across fair maids and evil doers. Adventures spring from politics, jealousy and greed, and the fact that Dunk is still learning his trade and finding his own way in the world.

What makes the story so engrossing is that Martin knows his medieval stuff. He knows about the tournaments of the times, the weaponry, the garments, and even the food. All this serves to bring the story alive.

And as I sit here typing this review I find myself wondering what Dunk and Egg are doing today. Which means that Martin has done his job admirable. He's made me like the setting and characters so much that I not only care about what great adventures they are working up to, but also about what they are doing when absolutely nothing much is going on.

Recommend.

~library find

76 of 84 people found the following review helpful.
Dragonflies or dragons?
By EA Solinas
One of the signs of a well-developed fantasy world is that the author isn't confined to the present -- they can spin stories from centuries ago, and it still fits their world perfectly. Such a tale can be found in the adventures of Dunk and Egg, which takes place a century before the events of George R. R. Martin's epic A Song Of Ice And Fire series. "A Knight of the Seven Kingdoms" brings together all three of those tales -- the story of a struggling hedge knight and his faithful yet snarky squire, both of whom are more than they appear.

In "The Hedge Knight," Dunk has spent most of his life as the squire to an aging hedge knight. But his master eventually dies, leaving him a sword, some old armor, a pair of horses and a handful of coins. He is determined to make a name for himself at a tourney, and hopefully be hired by some lord -- despite the annoyingly outspoken boy named Egg who is following him, hoping to become his squire.

But when Dunk arrives at the tourney, he finds that he's outmatched by everybody. He only need to score one win, but without armor he doesn't have a chance against the knights from powerful, aristocratic families. So he uses his wits to raise money... only to run afoul of a Targaryen prince when he defends a pretty puppeteer. Given the choice of losing his hand or trial by combat, Dunk chooses the less disfiguring option -- but that means he must find six allies to help him in the combat. And he might still lose his life at the end.

"The Sworn Sword" brings Dunk and Egg to Standfast, in service to Ser Eustace Osgrey. The land is in the middle of a horrifying drought, and when Dunk investigates the sudden drying-up of a nearby stream, he discovers that Osgrey's neighbor Lady Rohanne has built a dam to redirect a nearby river. There's a feud going on between Eustace and Rohanne, which will soon bloom into a vicious little war and the loss of Rohanne's lands -- and Duncan may be the only one who can resolve things once and for all.

"The Mystery Knight" sees Dunk and Egg entering an area full of political strife -- a rebellion some years ago has caused a lot of hostility towards the Targaryens. When invited to a wedding melee, Dunk decides to go as an unidentified "mystery knight" to avoid any people who might have heard of Ser Duncan the Tall... only to find himself and Egg the center of a conspiracy of murder, the theft of a dragon egg, and a brewing threat against his own squire.

Compared to A Song Of Ice And Fire, the three stories of "A Knight of the Seven Kingdoms" are relatively light fare -- sure, they have plenty of gritty medieval atmosphere and a few tragic deaths, but these stories are positively fairy-tale-like compared to the dark stuff Martin usually writes. Don't worry, there's still plenty of political strife, mysteries, battles and the odd speck of the fantastical (a dragon's egg, a knight with precognitive dreams), but the main focus is on a simple hedge knight and his squire.

Martin's writing is simple, but rich with descriptions ("Reeds grew thick along its edge, and a tall leafy elm presided over all. The spring grass there was as green as any knight’s banner and soft to the touch") and he writes some snappy dialogue between Dunk and Egg. Every story has an escalating story of political strife, with Dunk's seemingly simple existence repeatedly interrupted with the political repercussions of House Blackfyre -- he just wants to wander around and fight for whoever, sleeping under shady trees and eating salt beef, but he keeps being tripped up by the scars of the past.

And the approach to Dunk is what makes these stories so gripping. The man is a mass of endearing contradictions -- a gentle teenager who is also a powerful fighter, simple yet wise, intelligent yet straightforward, ambitious yet humble in those ambitions. His clear sight and good heart are what ultimately win people over to him, and even if he's kind of grumpy with Egg during the squire's more spoiled moments, there's not much actual bite to any of his complaints.

And what of Egg? I won't reveal too much about this character's life, but he is a perfect counterpoint to Dunk -- a boy torn between the glamorous idea of being a squire, and the actual struggles it involves. He has a nimble intellect and a smart mouth, but sometimes shows a distinct lack of common sense compared to Dunk.

"A Knight of the Seven Kingdoms" is a delightful trio of side-stories for fans of the Song Of Ice And Fire series, and a solid introduction to the land of Westeros for newcomers. And of course, it leaves you wanting to hear about more adventures of Dunk and Egg.

147 of 168 people found the following review helpful.
Great compilation prequel to Song of Ice and Fire with incredible artwork
By outwest
This is a compilation of the first three official prequel novellas to George R. R. Martin’s A Song of Ice and Fire, and take place almost one century before Game of Thrones. Note the material is not really new: this compiles the three novellas "The Hedge Knight" (Legends), "The Sworn Sword" (Legends II) and "The Mystery Knight" (Warriors). For those who follow the series, these novellas are set in the time when the Targaryens still hold the Iron Throne. A great collection of all three stories, this compilation also adds over 140 pieces of original artwork which will surely be a hit for fans of Martin's written works.

Though not as anticipated as the Song of Ice and Fire next installment, A Knight of the Seven Kingdoms is a nice look into the history of Targaryen rule. The book follow the friendship of "Dunk," Ser Duncan the Tall (future Lord Commander of the Kingsguard), and "Egg," (a pivotal character in the future). The story is similar to the knight-squire relationships found in Song of Ice and Fire (ie Brienne and Podrick) and reads similar to the Song of Ice and Fire series. The fantastic tale gives a deeper insight into the Seven kingdoms and the motivations for the people who are a part of them. Well written and with numerous new original illustrations, it is a great read.

Any Martin fan who has not already read the novellas will certainly enjoy it, and the artwork alone is fantastic. It is an in-depth genealogical journey and character study, interwoven in classic Martin style. Other than the illustrations, it is not new, but for those new to this story line and for fans of original artwork related to the overall series, will enjoy A Knight of the Seven Kingdoms.

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Minggu, 24 Juni 2012

[V840.Ebook] Download The Portable Beat Reader (Penguin Classics)From Penguin Classics

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The Portable Beat Reader (Penguin Classics)From Penguin Classics

Beginning in the late 1940's, American literature discovered a four-letter word, and the word was "beat." Beat as in poverty and beatitude, ecstasy and exile. Beat was Jack Kerouac touring the American road in prose as fast and reckless as a V-8 Chevy. It was the junk-sick surrealism of William Burroughs; the wild, Whitmanesque poetry of Allen Ginsberg; and the lumberjack Zen of Gary Snyder.

The Portable Beat Reader collects the most significant writing of these and fellow members (and spiritual descendants) of the Beat Generation, including Neal Cassady, Gregory Corso, Diane di Prima, Bob Dylan, Leroi Jones, and Michael McClure. In poetry, fiction, essays, song lyrics, letters, and memoirs, it captures the triumphant rudeness, energy, and exhilaration of a movement that swept through American letters with hurricane force.

For more than seventy years, Penguin has been the leading publisher of classic literature in the English-speaking world. With more than 1,700 titles, Penguin Classics represents a global bookshelf of the best works throughout history and across genres and disciplines. Readers trust the series to provide authoritative texts enhanced by introductions and notes by distinguished scholars and contemporary authors, as well as up-to-date translations by award-winning translators.

  • Sales Rank: #47866 in Books
  • Published on: 2003-07-29
  • Released on: 2003-07-29
  • Original language: English
  • Number of items: 1
  • Dimensions: 7.80" h x 1.10" w x 5.10" l, 1.01 pounds
  • Binding: Paperback
  • 688 pages

Amazon.com Review
The Portable Beat Reader is an excellent and thorough study of the Beat Generation, compiled and edited by Ann Charters, biographer of Jack Kerouac and one of our most notable experts on Beat literature and ideas. This lively work of scholarship goes deeply into the history of the Beat movement, investigating events such as the discovery (by writer William Burroughs) of the word beat to describe this literary generation. The reader includes essays on all the major prose and poetry writers, such as Allen Ginsberg, and offers rare insight into the literary-historical context of the movement.

From Publishers Weekly
Cutting through bohemian posturing and excess, Charters here reprints much of the most vital, readable and relevant material produced by the Beat generation, primarily in the 1950s and '60s, with some selections from the '70s and '80s. The novels of such leading figures as Jack Kerouac and William S. Burroughs lend themselves well to excerpting, giving this volume creditable ballast. Representative works of Allen Ginsberg, Lawrence Ferlinghetti and Gary Snyder are included along with those of lesser-known Beats (e.g., John Clellon Holmes), fellow travelers like Frank O'Hara and Amiri Baraka, and wives and girlfriends often overlooked at the time, including Hettie Jones, Carolyn Cassady and Joyce Johnson. Charters ( Kerouac ) offers a broad perspective on this seminal literary movement: she links East Coast Beats to the San Francisco Renaissance poets; pays attention to such latter-day Beats as Ed Sanders and Tuli Kupferberg; and explains the position of non-Beat but related writers--Alan Watts, Anne Waldman, Diane DiPrima and the young Norman Mailer--in her helpful introductory essay and notes preceeding each entry. Her energizing, liberating anthology makes it clear that such Beat preoccupations as the bomb, the meaninglessness of modern existence and ecological destruction remain current.
Copyright 1991 Reed Business Information, Inc.

From Library Journal
Charters, a noted critic of the Beat generation and Jack Kerouac's first biographer, has chosen a representative sample of Beat writings for the latest addition to the Viking "Portables" series. Arranged chronologically in six sections, it includes excerpts from Kerouac's On the Road , Allen Ginsberg's Howl , and William S. Burroughs's Naked Lunch , as well as work by Herbert Huncke, Ray Bremser, and lesser-known figures. "Beat" is used in its broadest sense to cover the San Francisco Renaissance poets and others, including Bob Dylan, who were influenced by or were sympathetic to the Beats. Robert Creeley's work is conspicuously absent. A general introduction as well as introductions to each section attempt to "situate the writers in their political, social, and literary contexts." This is highly recommended.
- William Gargan, Brooklyn Coll. Lib., CUNY
Copyright 1991 Reed Business Information, Inc.

Most helpful customer reviews

29 of 30 people found the following review helpful.
Good Start for the "Beat"en Path
By A.Trendl HungarianBookstore.com
Nice sampler... all the writers you should expect are in here.

A feature I enjoyed particularly was the intro section. Read "about the writer" before the piece. Since I had not heard of so many of these writers, I found it intriguing to see how many were part of the same group of friends. The Beats largely were connected with each other's lives and fed off each other's style and work.

Buy this book, then go get the full works of these individual writers. For an anthology of Beat writers, you will find no better book.

I fully recommend this book.

Anthony Trendl
anthonytrendl.com

13 of 13 people found the following review helpful.
A Great Guide If You Don't Know What You Like
By Autumn May
This book features some excellent beat writers and includes informative blurbs on their history and style. Each artist has a little chunk of their writing for you to sample, and the material is everything from stories to letters to classically bad prose.
What impressed me were the essays by each other, on the actual generation hype.
"Young people seemed more intense, clutching, and I couldn't help feeling they took themselves too seriously... 'good, clean fun' appeared to be a thing of the past. Or perhaps the aura of suspicion and defensiveness was merely a reflection of my own fears..." --Carylon Cassady
It's a great book for deciding which authors you want to read more of.

12 of 13 people found the following review helpful.
Introducing The Beats
By Marc Shaw
After riding the whirlwind of On The Road and Howl, many readers will acquire an appetite for Beat literature that will lead them to this book. Ann Charters serves up some of the best pieces of Beat Literature in this anthology. However, some inclusions, such as excerpts from On The Road, could be considered sacrilege, as the texts were never meant to be cut up, and suffer as a result. The Bob Dylan lyrics included by Charters indicate that she was no Dylan scholar, as other tracks would have been more suitable. However, with inclusions such as Neal Cassady's letters, William S. Burroughs adventure narratives, and the lesser known Beat poetry, this anthology is indispensable.

See all 33 customer reviews...

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40-30-30 Fat Burning Nutrition : The Dietary Hormonal Connection to Permanent Weight Loss & Better Health, by Joyce; Daoust, Gene Daoust

  • Sales Rank: #7290707 in Books
  • Published on: 1996
  • Binding: Paperback

Most helpful customer reviews

81 of 84 people found the following review helpful.
Awesome Weight loss and change of life!
By Scott
I started this plan over one year ago, and I am now 2/3 the man I used to be. I tipped the scales at over 320 pounds and have dropped 91 pounds to date through eating 40-30-30 and proper exercise. The diet plan is easy to follow and gives you more room for error than any other plan on the market. By that I mean it is easy for you to hop back on the plan if you slip up one meal or even one or two days. Once your body gets "Balanced" you maintain your weight, with a ton of new found energy. You begin to burn fat instead of storing it and your cravings become less and less a factor in dieting.
The addition of sample menus completes this easy to follow change in eating and helps keep you on track and focused. This book has fat flush meals, vegetarian fat flush meals and plain old 40-30-30 balanced meals, so pick one and get started. I won't kid you, the first few weeks were rough but after that the plan melted off, on average, 10 pounds per month and I'm still dropping pounds and inches with only 9 pounds to my goal weight. I've gone from a XXXL and XXL to an XL and from 46-48 pants to 36-38 pants. My heart rate at rest is 48 and my blood pressure is 116/72. I eat balanced meals and snacks when I want and exercise 4-5 times weekly, and my new found favorite snack is a "Balance" bar. I still have difficulty with Carbs, so I eat them before heading to the gym, and use that extra energy as a boost for a better workout!
This plan is awesome and comes highly recommended by me. Just watch your pocket book, because when you drop as much weight as I have a new wardrobe is a much needed and more than welcome expense!

58 of 60 people found the following review helpful.
Can't Believe I'm not Wanting Food All the Time!
By A Customer
Last night, while my husband sat a few feet from me, he ate brownies and I wasn't even tempted. I was still full from dinner. I feel like I'm on diet pills, but without the side-effects and cost. Prior to starting FBN, I was eating everything that wasn't nailed down. A little history: a couple of years ago I lost 21 lbs with The Zone. It really helped me understand why SOME people need less carbs and more protein. It was a lot to wade through, but very helpful. My doctor did blood work and found everything was super! I have II diabetes and HBP. Anyway, I somehow got out of the Zone, but never regained all my weight so I knew I was onto something. Recently I started the Fat Burning Nutrition because it was so similar, but easier to follow. Now, once again, my sugar levels are even, I'm feeling wonderful and most of all I'm not hungry all the time while eating regular, wholesome food. With all those good perks, I'm just gonna have to stay on this for life. Thanks, Joyce and Greg Daoust!

30 of 30 people found the following review helpful.
40-30-30- fat burning nutrition
By Rick Beese
I stumbled across this book by accident. Probably the best accident of my life. The book was an easy read, easy to follow, and the plan works outstanding. I have lost almost 45lb in the last 4 months, and thats with holiday cheating included. The science and theories were very interesting and really made alot of sense the way they were presented. The meal plans are clear, and include more meals and more food then I had eaten prior to the plan. I loved the sample menu planners and especially the food guide which allows you to make any substitutions you want to achieve the same result.
The theory and the whole plan are basic enough, that it allowed me, after just a week or two, to "know" what I was supposed to be eating and I have pretty much stopped using the book as a reference all together. But the pounds just keep falling off!

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Sabtu, 23 Juni 2012

[A747.Ebook] Ebook Download Jack O' Lantern: A Halloween Tale, by Eric Martone

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Jack O' Lantern: A Halloween Tale, by Eric Martone

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Jack O' Lantern: A Halloween Tale, by Eric Martone

'Stingy' Jack, the town rogue, is disliked by everyone.

One Halloween night, Jack is met by the Devil, who informs Jack that his soul is to be taken to Hell.

A battle of wits ensues as 'Stingy' Jack tries to keep his soul by outsmarting the Devil. Based on an old Irish folktale, Jack O'Lantern explains the origin of the traditional Halloween symbol that bears the same name.

  • Sales Rank: #16349873 in Books
  • Published on: 2003-07-17
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.02" h x .13" w x 5.98" l,
  • Binding: Paperback
  • 56 pages

Most helpful customer reviews

0 of 0 people found the following review helpful.
Enjoyable holiday tale!
By A Customer
From the back of the book:

'Stingy' Jack, the town rogue, is disliked by everyone.
One Halloween night, Jack is met by the Devil, who informs Jack that his soul is to be taken to Hell.
A battle of wits ensues as 'Stingy' Jack tries to keep his soul by outsmarting the Devil. Based on an old Irish folktale, Jack O'Lantern explains the origin of the traditional Halloween symbol that bears the same name.

1 of 1 people found the following review helpful.
Cute story!
By A Customer
From the back of the book:
'Stingy' Jack, the town rogue, is disliked by everyone.
One Halloween night, Jack is met by the Devil, who informs Jack that his soul is to be taken to Hell.
A battle of wits ensues as 'Stingy' Jack tries to keep his soul by outsmarting the Devil. Based on an old Irish folktale, Jack O'Lantern explains the origin of the traditional Halloween symbol that bears the same name.

See all 2 customer reviews...

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Senin, 18 Juni 2012

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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, by Cathy O'Neil

Longlisted for the National Book Award
New York Times Bestseller

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.

  • Sales Rank: #1917 in Books
  • Published on: 2016-09-06
  • Released on: 2016-09-06
  • Original language: English
  • Number of items: 1
  • Dimensions: 8.56" h x .95" w x 5.80" l, 1.25 pounds
  • Binding: Hardcover
  • 272 pages

Review
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

“Weapons of Math Destruction is an urgent critique of… the rampant misuse of math in nearly every aspect of our lives.”
—Boston Globe

“Illuminating… [O’Neil] makes a convincing case that this reliance on algorithms has gone too far.”
—The Atlantic

“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 modelling 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

“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
 
“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
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 appears weekly on the Slate Money podcast.

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.

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86 of 90 people found the following review helpful.
Stop Using Math as a Weapon
By Amazon Customer
So here you are on Amazon's web page, reading about Cathy O'Neil's new book, Weapons of Math Destruction. Amazon hopes you buy the book (and so do I, it's great!). But Amazon also hopes it can sell you some other books while you're here. That's why, in a prominent place on the page, you see a section entitled:

Customers Who Bought This Item Also Bought

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.

5 of 5 people found the following review helpful.
Models without Morality
By Graham Webster
This is a well written book about the data science algorithms that can influence our lives. If all they were responsible for was recommending other books we might be interested in reading, then the topic wouldn't be very important. However, they influence an increasing number of areas in our lives, from insurance pricing, college admissions to hiring decisions, so it is important that the assumptions that underlay these models be explicit and transparent. That's mostly not the case, and in fact, many models take into account data for which it is not legal for a human to take account of. The black box nature of these models is a concern.

I think O'Neil makes a solid argument against the unregulated use of these black box models in society.

O'Neil touches on the issue of the lack of ethics in the tech industry, where smart people are focused on making money and lose sight of the negative impact their work has on much of society.

4 of 4 people found the following review helpful.
Personally I would have liked to see some of the types of algorithms that ...
By Peter
Well written, with no math needed to understand the problems with bias, discrimination and injustice that Cathy exposes. Personally I would have liked to see some of the types of algorithms that Cathy describes, but that's because I was a math major in college - and that's the only reason I didn't give it 5 stars.

The examples of school teachers being judged on invalid assessments, and likely cheating of the system by other teachers to make sure they don't get fired really hit home as to how bad the mathematical formulas can be in deciding who gets or keeps a job. I'm familiar with the hiring algorithm practices of one of the companies Cathy describes in her book, and I heard the flip side of the rejection process from one of its employees. Supposedly after taking the psychological application test, if a candidate does really well, an alert is sent to the hiring manager in real time to go meet the candidate to offer them a job before they leave the building. I hope the pending class action lawsuit she describes against this company succeeds because there is obvious discrimination going on in the hiring of applicants and many large corporations now use this same employment screening software.

Cathy exposes injustices to average people in many ways they live in our world, from education to employment, credit and financial dealings. Her final chapter is a call for reform of hidden practices that rely on fuzzy math algorithms. It is very important that we can see and understand how decisions are being made about our future, and on what information those decisions are based. If bad data is being used we should be able to correct that. This is definitely needed in the same way that consumers can get to see their credit reports, and report errors to get them corrected.

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The Blame Game: How the Hidden Rules of Credit and Blame Determine Our Success or Failure

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