--- product_id: 46082838 title: "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy" price: "S/.72" currency: PEN in_stock: true reviews_count: 13 url: https://www.desertcart.pe/products/46082838-weapons-of-math-destruction-how-big-data-increases-inequality-threatens store_origin: PE region: Peru --- # Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy **Price:** S/.72 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy - **How much does it cost?** S/.72 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.pe](https://www.desertcart.pe/products/46082838-weapons-of-math-destruction-how-big-data-increases-inequality-threatens) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description NEW YORK TIMES BESTSELLER • A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric—with a new afterword “A manual for the twenty-first-century citizen . . . relevant and urgent.”— Financial Times NATIONAL BOOK AWARD LONGLIST • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review • The Boston Globe • Wired • Fortune • Kirkus Reviews • The Guardian • Nature • On Point We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules. But as mathematician and data scientist Cathy O’Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination—propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data. Review: Must read for all aspiring Data Scientists. - Welcome to the cruel reign of highly efficient algorithms! Yay... In short, this is an excellent albeit very high-level overview of the most pressing techno-moral issues at the core of advancements in Machine Learning, AI, and the many obscure mathematical models quietly ruining running our lives. Be advised, those looking for mathematical exposition or in-depth explanations about the models mentioned herein will be better served elsewhere. As a data-science/machine-learning practitioner, I found O'Neil's case and her supporting material both edifying and deeply concerning. You see, I had heard stories of algos running amok, kicking asses and taking names in the all consuming search for optimizing ways to squeeze cents out of each byte of data comprising our cyber identities, but the extent of the chicanery employed by the companies and their analysts in their approach is just so deliciously evil that you would think they're secretly engineered by cats. While I found much of the book solidly researched and cogent in its underlying argument, from time to time I did find some minor quibbles with her points. For instance, early on in the text she recounts her time as a quantitative analyst at a high-caliber Wall Street hedge fund, where she ultimately came to the conclusion that it was the insidious power of math that engendered much of the chaos that resulted in the financial crisis of 2008. However, not five pages later, she mentions leaving said fund to go work for an investement risk consultancy firm, where her team's detailed analysis would go unheeded by the very same firms employing them (they just needed to look like they were being responsible by carrying out due diligence). So, it's not that the math was bad, or that the models failed to take into account this or that variable, it's just that the guys running the show knew the risks but decided to gamble on them anyways. This theme is repeated throughout, as her case studies expose a deep disregard on the part of the algo overlords to rectify unfair practices unless legally obliged to do so. One can see how deeply flawed this attitude is and where it may lead us, especially under the mercy of an arguably lethargic political system; random fact: in my home country there's a saying, "hecha la ley, hecha la trampa", which roughly translates to "by the time the law is written, a new snare is already in place". Traditional politicians will never keep up with the tech sector. Which brings me to the saddest part of the book, which is the author's attempt to lay down a blueprint for bringing much needed change. I can tell she deeply cares about the issues at the core of her argument, but I'm just not that convinced any of them could ever work without somehow making it simultaneously profitable to the companies involved. All in all, I think most of us would do well to give this read if only to get a sense of what's at stake here, and how we ultimately came to be unwilling participants in this curve-fitting, dot-connecting, profits-above-all game. Review: Interesting. Lots of Books on the Topic. Maybe it’s “the flavor of the month” - Very interesting. Definitely has a POV. U think that’s obvious from the title but it’s more than that. Can lead to some tangents but not so much so that it takes away from the book. Was a very quick very easy read. Not technical. Not dumbed down. Given the authors background more history and background would’ve been nice. We all know where we are and have a decent sense about how we got here and what’s been going on recently. But it’s time to trace things back to their origins. When FB and Google took command of data, when they monetized it. What was going on before social media. What role gov’t has, had, or doesn’t have in this. Both how things got how they are and where they’re going. Did the market act to crate these conditions. Is there something the gov’t did or didn’t do, or were there unforeseen or unintended consequences of gov’t action or laws on the books pre social media and pre Internet. Right now cigarettes can’t advertise on TV. But e-cigarettes can. Nicotine is the same but one way TV the other way no TV. Are there examples like that which apply to algorithms, data collection, social media, corporate or individual responsibility. There was so much more to cover. The chapters in the book could have been pared down a little to allow for this info in the same amount of space. Or just include it as well. There’s a lack of true context. And a lack of a real solution. How math can solve the problem. Or can’t. It’s not enough that people might not be interested in actually solving it. Or is it enough that u don’t have to know math and u can be part of a resolution. U don’t need to know how to do graduate math if there’s a simple answer like “don’t do ____” and that keep u out of the weapons the author is concerned with. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #30,725 in Books ( See Top 100 in Books ) #5 in Business Statistics #6 in Privacy & Surveillance in Society #7 in Data Processing | | Customer Reviews | 4.4 out of 5 stars 5,037 Reviews | ## Images ![Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy - Image 1](https://m.media-amazon.com/images/I/71UCr1GIFrL.jpg) ## Available Options This product comes in different **Media Language, Media Format** options. ## Customer Reviews ### ⭐⭐⭐⭐⭐ Must read for all aspiring Data Scientists. *by W***. on January 29, 2019* Welcome to the cruel reign of highly efficient algorithms! Yay... In short, this is an excellent albeit very high-level overview of the most pressing techno-moral issues at the core of advancements in Machine Learning, AI, and the many obscure mathematical models quietly ruining running our lives. Be advised, those looking for mathematical exposition or in-depth explanations about the models mentioned herein will be better served elsewhere. As a data-science/machine-learning practitioner, I found O'Neil's case and her supporting material both edifying and deeply concerning. You see, I had heard stories of algos running amok, kicking asses and taking names in the all consuming search for optimizing ways to squeeze cents out of each byte of data comprising our cyber identities, but the extent of the chicanery employed by the companies and their analysts in their approach is just so deliciously evil that you would think they're secretly engineered by cats. While I found much of the book solidly researched and cogent in its underlying argument, from time to time I did find some minor quibbles with her points. For instance, early on in the text she recounts her time as a quantitative analyst at a high-caliber Wall Street hedge fund, where she ultimately came to the conclusion that it was the insidious power of math that engendered much of the chaos that resulted in the financial crisis of 2008. However, not five pages later, she mentions leaving said fund to go work for an investement risk consultancy firm, where her team's detailed analysis would go unheeded by the very same firms employing them (they just needed to look like they were being responsible by carrying out due diligence). So, it's not that the math was bad, or that the models failed to take into account this or that variable, it's just that the guys running the show knew the risks but decided to gamble on them anyways. This theme is repeated throughout, as her case studies expose a deep disregard on the part of the algo overlords to rectify unfair practices unless legally obliged to do so. One can see how deeply flawed this attitude is and where it may lead us, especially under the mercy of an arguably lethargic political system; random fact: in my home country there's a saying, "hecha la ley, hecha la trampa", which roughly translates to "by the time the law is written, a new snare is already in place". Traditional politicians will never keep up with the tech sector. Which brings me to the saddest part of the book, which is the author's attempt to lay down a blueprint for bringing much needed change. I can tell she deeply cares about the issues at the core of her argument, but I'm just not that convinced any of them could ever work without somehow making it simultaneously profitable to the companies involved. All in all, I think most of us would do well to give this read if only to get a sense of what's at stake here, and how we ultimately came to be unwilling participants in this curve-fitting, dot-connecting, profits-above-all game. ### ⭐⭐⭐⭐ Interesting. Lots of Books on the Topic. Maybe it’s “the flavor of the month” *by P***N on March 4, 2021* Very interesting. Definitely has a POV. U think that’s obvious from the title but it’s more than that. Can lead to some tangents but not so much so that it takes away from the book. Was a very quick very easy read. Not technical. Not dumbed down. Given the authors background more history and background would’ve been nice. We all know where we are and have a decent sense about how we got here and what’s been going on recently. But it’s time to trace things back to their origins. When FB and Google took command of data, when they monetized it. What was going on before social media. What role gov’t has, had, or doesn’t have in this. Both how things got how they are and where they’re going. Did the market act to crate these conditions. Is there something the gov’t did or didn’t do, or were there unforeseen or unintended consequences of gov’t action or laws on the books pre social media and pre Internet. Right now cigarettes can’t advertise on TV. But e-cigarettes can. Nicotine is the same but one way TV the other way no TV. Are there examples like that which apply to algorithms, data collection, social media, corporate or individual responsibility. There was so much more to cover. The chapters in the book could have been pared down a little to allow for this info in the same amount of space. Or just include it as well. There’s a lack of true context. And a lack of a real solution. How math can solve the problem. Or can’t. It’s not enough that people might not be interested in actually solving it. Or is it enough that u don’t have to know math and u can be part of a resolution. U don’t need to know how to do graduate math if there’s a simple answer like “don’t do ____” and that keep u out of the weapons the author is concerned with. ### ⭐⭐⭐⭐⭐ Must read, especially for students of engineering and computer science *by J***I on October 23, 2016* This is a thoughtful and very approachable introduction and review to the societal and personal consequences of data mining, data science, and machine learning practices which seem at times extraordinarily successful. While others have breached the barriers of this subject, Professor O'Neil is the first to deal with it in the call-to-action manner it deserves. This is a book you should definitely read this year, especially if you are a parent. It should be required reading for anyone who practices in the field before beginning work. I have a few quibbles about the book's observations based on its very occasional leaps of logic and some quick interpretations of history. For example, while I wholeheartedly deplore the pervasive use of e-scores and a financing system which confounds absence of information with higher risk (that is, fails to posit and apply proper Bayesian priors), the sentence "But framing debt as a moral issue is a mistake", while correct, ignores the widespread practice of debtors courts and prisons in the history of the United States. This is really not something new, only a new form. Perhaps it is more pervasive. For a few of the cases used to illustrate WMDs, there are other social changes which exacerbate matters, rather than abused algorithms being a cause. For instance, the idea of individual home ownership was not such a Big Deal in the past, especially for people without substantial means. These less fortunate individuals resigned themselves to renting their entire lives. Having a society and a group of banks pushing home ownership onto people who can barely afford it sets them up for financial hardship, loss of home, and credit. What will be interesting to see is where the movement to fix these serious problems will go. Protests are good and necessary but, eventually, engagement with the developers of actual or potential WMDs is required. An Amazon review is not a place to write more of this, nor give some of my ideas. Accordingly, I have written a full review at my blog (see the image) for the purpose. My primary recommendation is a plea for rigorous testing of anything which could become a WMD. It's apparent these systems touch the lives of many people. Just as in the case of transportation systems, it seems to me that we as a society have very right to demand these systems be similarly tested, beyond the narrow goals of the companies who are building them. This will result in fewer being built, but, as Dr O'Neil has described, building fewer bad systems can only be a good thing. ## Frequently Bought Together - Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy - Co-Intelligence: Living and Working with AI - Dataclysm: Love, Sex, Race, and Identity--What Our Online Lives Tell Us about Our Offline Selves --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.pe/products/46082838-weapons-of-math-destruction-how-big-data-increases-inequality-threatens](https://www.desertcart.pe/products/46082838-weapons-of-math-destruction-how-big-data-increases-inequality-threatens) --- *Product available on Desertcart Peru* *Store origin: PE* *Last updated: 2026-10-11*