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Weapons of Math Destruction Audiobook

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

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Audible Editor Reviews

"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." (Steven Strogatz, Cornell University, author of The Joy of x)

Publisher's Summary

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 troublingly, 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 paroles, 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 savvier about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.

©2016 Cathy O'Neil (P)2016 Random House Audio

What the Critics Say

"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 )

"Weapons of Math Destruction shines invaluable light on the invisible algorithms and complex mathematical models used by government and big business." (Astra Taylor, author of The People's Platform)

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  •  
    Laurent Bourgault-Roy Canada 01-08-17
    Laurent Bourgault-Roy Canada 01-08-17 Member Since 2015
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    "More are US social problems that WMD"

    A WMD, or weapon of math destruction, are usage of algorithm that end up being discriminatory toward some people, or that cause problem with their wide scale deployment. For example, an algorithm that identify poor people can deny them services that help them, making them poorer. The algorithm prediction become self-fulfilling and prevent people from improving their condition.  

    The premise of the book is very good, and there are indeed a lot of good example of how misuse of big data algorithms can wreak havoc among society. The problem is that the author indignation push her away from what should have been the main subject of the book. 

    In the course of the book, the author raise a lot of recurring problem with WMD, like the "Flock of the feathers" generalization, the "self-fulfilling" prediction, the "discriminating proxy variable ", the "non-appealable conclusion" problem, the "non-measurable important factor". But those categories of problem, which, in my opinion, should have been the focus of the book, take a backseat toward the real subject of the book: how much the United State has social problems.

    Each chapter is written to for denounce a specific social problem in the US, like predatory ads toward the poor, racial discrimination toward minority, terrible working hour among low wage workers, and so on. Some of those subjects are indeed caused by WMD. But for some, the link with the purported subject of the book is a bit strenuous. In some case, the author even exclaims "well, that has nothing to do with WMD of course". And a lot of time, WMD are not the root cause of the problem, they only exacerbate an existing one. 

    That leave you with a book that is more like a classical sociology book denouncing the ill of the American society, with some talk about big data sprinkled on top. If, like me, you are not an American, you may feel a bit left out by that book. This is a shame, because by refocusing the book on the generic problem caused by WMD that I described above, the book could have had a much broader appeal. Don't get me wrong: The problem O Neil talk about ARE important social problem. But they are very specific to her own country, and the militant tone can become grating. I felt at time that the author was not explaining to me how WMD work and how to deal with them, but was rather trying to force her opinion of how the world should be over me. She was dictating me how I should think, rather than helping me shape my own opinion.

    In the end, I would have preferred a more objective tone and a better focus on WMD themselves, with conclusion that can be applied more broadly to everyone, not just US citizens.

    12 of 13 people found this review helpful
  •  
    Derek 09-13-16
    Derek 09-13-16 Member Since 2017
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    "a must read for the modern economy"

    O'Neil makes a strong case for the increasing importance of ethics in data science. The evidence for discrimination, whether intentional or not, is compelling. This book is a must for data professionals and anyone concerned with growing inequality in the economy.

    4 of 4 people found this review helpful
  •  
    Stephen 10-02-16
    Stephen 10-02-16 Member Since 2017
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    "A fascinating and startling look at where big data is blind"

    This book is totally worth the listen for the intro and first chapter alone. It's very well-written and easy to follow, and manages to tell clear stories about how the software we use to assess teacher performance or insurance risk is all to often encoded with the prejudices and blind spots of the people who make it. It shows how that is already damaging equality and democracy, and warns of areas where it may get worse.

    As a software designer, the one thing I would have loved from this book would be a little more depth about how software products might avoid these pitfalls. However, I'm probably coming at this book with unfair expectations, and it's likely a subject I just need to research more deeply.

    Overall, if you enjoy podcasts like Freakonomics and Planet Money, you'll probably love this. Happy I listened!

    3 of 3 people found this review helpful
  •  
    Zwelithini 09-14-16
    Zwelithini 09-14-16 Member Since 2017
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    "Good book"

    I like Cathy's writing and analysis. I wish they had gotten a professional reader though, it would have made it more enjoyable. It's not as if Cathy is awful or anything, she is just not professional.

    2 of 3 people found this review helpful
  •  
    Michael 09-10-16
    Michael 09-10-16 Member Since 2017
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    "Experienced Insight and Issues Identified"

    The story is presented in a series of topics disclosures that compare by theme, data models can be made and used in ways that can damage society and make bad situations worse. Cathy O'Neil reveals how data models can be relied on with good intentions in mind, and by ignorance, dismissal or narrow-sightedness, can misrepresent, injure and derail people and societal function-ability.

    3 of 4 people found this review helpful
  •  
    Kacper 09-06-16
    Kacper 09-06-16 Member Since 2016
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    "Superb narration, beautifully written. "

    This is a must read! I thoroughly enjoyed the real world examples of how everything I do is a data point that is being used against me.

    6 of 8 people found this review helpful
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    Amazon Customer 09-18-17 Member Since 2016
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    "Intriguing"

    While intriguing and insightful, in some instances the author may suffer from her own confirmation bias (an issue she attacks in the book). It's worth the listen, do not treat this book, or it's biases, as doctrine.

    0 of 0 people found this review helpful
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    Victor L. Marsh, II Detroit, MI United States 09-14-17
    Victor L. Marsh, II Detroit, MI United States 09-14-17 Member Since 2017
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    "Engagingly told"

    This is an excellent introduction to the practical impact of mathematical models in modern society. It's not just about the economic sectors, but also judicial, and in education too. The author has a point of view, but at least address other ways of interpreting things directly (and convincingly, I think).

    For future directions: there are challenges facing both the left and the right in terms of acting on the recommendations of this excellent book. Both sides claim they want people to have freedom. Ironically, the most tech-friendly folks (the left) are also least concerned about its monopoly power. On the other side, the most freedom-loving folks (the right) are also least concerned about locking up minorities or unfairly punishing teachers with bad math models.

    What remains is a pathway in which both sides are hoodwinked into believing that the author's bold ideas might serve their worst biases. That's always a tall order in public policy. It's a worthy future project for those who have the technical skills and political connections to act on the author's excellent recommendations and well-argued perspectives.

    0 of 0 people found this review helpful
  •  
    K. Donckels California 08-23-17
    K. Donckels California 08-23-17 Member Since 2015
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    "Fascinating and Timely Subject"

    Delves into the discipline of meta data mining performed by computer algorithms in laymen terms.

    1 of 1 people found this review helpful
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    jro 08-03-17
    jro 08-03-17
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    "Good intentions but horribly biased"

    I totally agree with what the author tries to achieve and for this I actually somewhat support the book. However, this book is not really about big data or the perils of various predictive models based on them. It's simply about human nature, human greed and human stupidity. I've learned here a great deal about the pains and problems of the US education system, prison system, justice system, hiring system etc., with the book claiming that behind all these there are very dangerous mathematical models. But in fact you realize that it is over and over again about how mean a human being can be to other human beings and how this can be scaled up by human ignorance. Mathematical models per se play a very secondary role in this - only the author of the book is kind of obsessed with blaming them, which has become a very sexy opinion and argumentative practice towards laymen of mathematics or data science.

    0 of 1 people found this review helpful

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