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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

by Cathy O'Neil

Other authors: See the other authors section.

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1,740969,997 (3.84)50
"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. "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."--Dust jacket.… (more)
  1. 00
    Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble (johnxlibris)
  2. 00
    Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz (alco261)
    alco261: Everybody Lies leans a bit optimistic, Weapons of Math Destruction leans a bit pessimistic - together they do a great job of providing a balanced understanding of big data issues
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Showing 1-5 of 93 (next | show all)
A powerful explanation of how algorithms and statistical models are used in various aspects of financial and public life in the United States, although some of them have the potential to cause a lot of harm. I don't think there was much in this book that I hadn't read about before, but it was well written and served as a useful reminder. ( )
  mari_reads | May 10, 2024 |
Important subject, but not as deep or engaging as I was hoping for. I found myself skimming chapters more often than I wanted to. It feels like a New Yorker column that got fleshed out to book length - good, but not gripping. ( )
  patl | Feb 29, 2024 |
If data were money, would you let Mark Zuckerberg run for President of the United States? This question jumped out at me while I was finishing Cathy O'Neil's provocative screed "Weapons of Math Destruction."

This spring the Zuckerberg family took a Great American Road Trip ostensibly to see how other Americans were faring under technological trends, and presumably under the Trump administration. Zuckerberg himself was quick to dismiss press speculation that the trip was intended to gather data on Zuckerberg's chances in the 2020 US elections.

Still the question remains an open one: how "fit" would a Silicon Valley oligarch be considered for the nation's highest office.

Based upon my reading of O'Neil's book, the answer is clear: Americans -- and the rest of us (I am not American) -- are rapidly coming under the thumb of big data; that big data's grip is stronger on socially and economically disadvantaged people; and the bigger the data the more valuable the resource.

An illustration would do here: an insurance company gathers data on where you drive your car, your propensity for jackrabbit starts at intersections, and how quickly you motor down side streets. The insurance company makes you an offer: manage your car a little more safely and we'll drop your insurance premiums by, say, 6%.

In this scenario, providing access to your data -- providing access to your private information -- gives you a clear financial incentive. I know exactly how much money I can save by modifying my driving habits.

Here's another scenario: your employer offers you a disincentive to continue smoking, eating pizza five times a week, and guzzling eight beers a day at dinner. Your company-run health insurance premiums will rise and be deducted from your pay.

This is the power that big data wields today. Not next week, not next decade.

As CEO of Facebook and its gargantuan unique data resources, Zuckerberg is far wealthier than his monetary wealth would suggest. That alone should make him unfit to contest an election.

Data is wealth. Big time.

But US election laws do not recognize data as having any influence on the outcome of elections even if you and I both know that data is bankable. Sources of money must be documented -- to a degree. Connections with foreign powers must be disclosed. But access to data? Control over algorithms? Nary a word.

If we wonder why our political leaders seem to be so weak in the face of economic pressures, social trends, and technology, it's because most of our leaders are data poor. They must beg and pay for data at the trough of the most powerful.

O'Neil rightly points out that privacy has a very real price, a price that only wealthy will be able to pay in the future, and even the wealthy participate in the data sharing. Poor people, young people looking for their first job, convicted felons, families looking for financing on their first home, new drivers, high school graduates wanting admission to elite universities, and even school teachers will have no choice. Big data will extract economic rents on them to give them access.

O'Neil calls them the new tribes -- may we call them "data tribes"? -- groups who are constructed by algorithms to whom are sold a product, or an idea, or to weed out from exclusive clubs.

Even today there are wellness penalties for not participating, teacher metrics, recidivist forecasting, crime forecasting, recruiting screens, and credit scores.

Facebook and Google algorithms are massive, powerful, and opaque. Detecting bias in the computer algorithms will be among our greatest challenges in the post-information age.

In 2020, the Republicans will have a serious problem with Donald Trump. Commentators have focussed on the trouble Democrats will have unseating Trump, but I think that if Republicans want a primary challenge to Trump they are going to have to go deep or go home.

Is a Zuckerberg or Bezos the way they need to go? Well, Peter Thiel backed Trump.

O'Neil muses that dumping the electoral college system would be one way to protect the republic and i would have to agree. The electoral college protects the bias built into the US election system toward state interests which are clearly anti-democratic.

States shouldn't rule.

Should data tribes? ( )
  MylesKesten | Jan 23, 2024 |
This book is well written and quite easy to read. It's unfortunate that the author does not recommend any practical strategies to deal with the inequalities and threats. ( )
  gregheth | Nov 24, 2023 |
I'm not really the audience for this book, since I work in the field and have been reading about the awful misapplication of algorithms for years. The tone is a little too pop-science-gee-whiz for my taste, but O'Neil does a thorough job of exploring the many ways in which the methods of the field can be abused. ( )
1 vote mmparker | Oct 24, 2023 |
Showing 1-5 of 93 (next | show all)
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» Add other authors (9 possible)

Author nameRoleType of authorWork?Status
Cathy O'Neilprimary authorall editionscalculated
Marty, SébastienTranslatorsecondary authorsome editionsconfirmed
Villani, CédricPrefacesecondary authorsome editionsconfirmed
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"When I was a little girl, I used to gaze at the traffic out the car window and study the numbers on license plate. I would reduce each one to its basic elements -- the prime numbers
that made it up. 45 = 3 x 3 x 5. That's called factoring, and it was my favorite investigative pastime. As a budding math nerd, I was especially intrigued by the primes."
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"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. "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."--Dust jacket.

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