Nonfiction

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

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

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  • Create Date：2021-03-05 03:13:09
  • Update Date：2025-09-06
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  • Author：Cathy O'Neil
  • ISBN：0141985410
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Summary

New York Times Bestseller

'Fascinating and deeply disturbing' - Yuval Noah Harari， Guardian Books of the Year

'A manual for the 21st-century citizen。。。 accessible， refreshingly critical， relevant and urgent' - Federica Cocco， Financial Times

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 loan， how much we pay for 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。

And yet， as Cathy O'Neil reveals in this urgent and necessary book， the opposite is true。 The models being used today are opaque， unregulated， and incontestable， even when they're wrong。 Most troubling， they reinforce discrimination。 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 CVs， grant or deny loans， evaluate workers， target voters， and monitor our health。

O'Neil calls on modellers 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。

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Reviews

Sjors De ，

Couldnt get through this book。 Must be highly interesting for people living in the US but the mechanisms she described are not applicable to mainland Europe。 Moreover she is very negative and does not mention the positive elements of AI。

Aaronames ，

Interesting take on data， well worth the read

Jason McCoy ，

One misconception you might have right now is the objective nature of computer algorithms。 Cathy O’Neil’s Weapons of Math Destruction first shows you the pinnacle of algorithm objectivity: baseball。 Baseball math and algorithms are transparent， measure the event， and are responsive to feedback。Then， O’Neil pans the camera away to the horror of algorithms that are opaque， rely on proxies， and rarely incorporate feedback: education， finance， mortgages， predictive policing， recidivism， and insuranc One misconception you might have right now is the objective nature of computer algorithms。 Cathy O’Neil’s Weapons of Math Destruction first shows you the pinnacle of algorithm objectivity: baseball。 Baseball math and algorithms are transparent， measure the event， and are responsive to feedback。Then， O’Neil pans the camera away to the horror of algorithms that are opaque， rely on proxies， and rarely incorporate feedback: education， finance， mortgages， predictive policing， recidivism， and insurance。 Computer algorithms do not turn their inputs into objective facts。 Computer algorithms amplify the biases of the programmer and the dataset。 We need to develop a societal understanding of the tools that automate our lives or we’ll forever be manipulated by them。“Late at night， a police officer finds a drunk man crawling around on his hands and knees under a streetlight。 The drunk man tells the officer he’s looking for his wallet。 When the officer asks if he’s sure this is where he dropped the wallet， the man replies that he thinks he more likely dropped it across the street。 Then why are you looking over here？ the befuddled officer asks。 Because the light’s better here， explains the drunk man” （Source: these exact words have 88 Google results）。Weapons of Math Destruction: https://slpl。bibliocommons。com/item/s。。。 。。。more

Asya ，

Very disappointing。 This was just a rant about a rather random selection of cases where the use of ‘big data’ or ‘mathematical models’ referred arbitrarily as weapons of math destruction， have had a negative effect on society or particular group of individuals。 It gives no constructive feedback or advice how to avoid this and only touches briefly on possible actual underlying problems of each case （in e。g。 insurance， education， financial or law enforcement areas）。 Often the issues originate from Very disappointing。 This was just a rant about a rather random selection of cases where the use of ‘big data’ or ‘mathematical models’ referred arbitrarily as weapons of math destruction， have had a negative effect on society or particular group of individuals。 It gives no constructive feedback or advice how to avoid this and only touches briefly on possible actual underlying problems of each case （in e。g。 insurance， education， financial or law enforcement areas）。 Often the issues originate from the lack of policies， biased practices to start with and no data privacy regulations。 These are important relevant issues， but the book lacks appropriate context to the cases of wrong usage of big data。 Nor does it give any significant insights how to handle big data and mathematical models to avoid them being used a ‘weapons of math destruction’。 。。。more

Ramón Cornejo-Muñoz ，

Libro Semana 10/52: "Armas de destrucción matemática: Cómo el big data aumenta la desigualdad y amenaza la democracia"， de Cathy O'Neil， muestra el como las Armas de Destrucción Matemática （ADM） que son los distintos modelos matemáticos que son utilizados para generar políticas， tanto públicas como privadas， impactan los mismos sistemas creando incentivos perversos。 Elecciones， seguros， educación y muchos otros son los ejemplos que la autora utiliza para mostrar lo complejo de su utilización。Mie Libro Semana 10/52: "Armas de destrucción matemática: Cómo el big data aumenta la desigualdad y amenaza la democracia"， de Cathy O'Neil， muestra el como las Armas de Destrucción Matemática （ADM） que son los distintos modelos matemáticos que son utilizados para generar políticas， tanto públicas como privadas， impactan los mismos sistemas creando incentivos perversos。 Elecciones， seguros， educación y muchos otros son los ejemplos que la autora utiliza para mostrar lo complejo de su utilización。Mientras lo leía， me daba cuenta de que muchas veces nosotros， los que utilizamos datos para obtener conclusiones y generar recomendaciones， no nos damos cuenta de que estos mismos resultados generan cambios en el comportamiento de las personas， haciendo que esta misma recomendación pueda ser manipulada por algunos grupos de personas en su beneficio。 En general， lo que resume el espíritu de este ensayo se encuentra en sus últimas líneas: "Los modelos matemáticos ampliamente utilizados - basados en prejuicios， malentendidos y parcialidad - con sus desalentadores algoritmos que rigen todos los aspectos de nuestras vidas tienden a castigar a los pobres y recompensar a los ricos"。Recomendable para quienes tenemos en nuestras manos una de las fuentes de poder más impactantes de este siglo: los datos y la información。 Dejemos de oscurecer nuestros algoritmos y hagámoslos tan transparentes que cualquier política pueda ser auditada en todo momento， sin dejar que esta pseudo-falacia de lo inmaculados que son los modelos matemáticos。 Así no permitamos que los prejuicios se trasladen a herramientas que， literalmente， son el futuro。#armasdedestruccionmatematica #adm #cathyoneill #readingchalleng #goodreads 。。。more

Marco ，

This books focus on the impact that certain statistical model can have on people when deployed without thinking about the impact they can have on individuals。 Overall a nice read but not a must

Marin Gamboa ，

Weapons of Math Destruction is a fantastic read that goes over the dangerous feedback loops automatic processing， done using black-box algorithms that go without critique， can have in cementing and perpetuating the inequalities in society。 With half-baked statistical models and the assumed infallibility of algorithms; the harms these perpetuating assumptions carry to society， especially those less privileged， are worthy of granting the （admittedly grandiose title） of 'weapons of MATH destruction Weapons of Math Destruction is a fantastic read that goes over the dangerous feedback loops automatic processing， done using black-box algorithms that go without critique， can have in cementing and perpetuating the inequalities in society。 With half-baked statistical models and the assumed infallibility of algorithms; the harms these perpetuating assumptions carry to society， especially those less privileged， are worthy of granting the （admittedly grandiose title） of 'weapons of MATH destruction'。 O'Neil touches on topics from all parts of life from college admissions， job interviews， insurance， and racial profiling/surveillance。 "Big Data processes codify the past"， the future thus needs to be created by individuals。 To counteract these weapons of MATH destruction O'Neil proposes industry self-regulation， data scientist's hypocratic oaths， additional positive feedback loops to feed into these black-box algorithms， transparency， and law changes。 These solutions are presented in the conclusion of the book and the specifics are lacking。 I was happy to read those suggested solutions， but disappointed that they were not expanded upon。 。。。more

John ，

I really wanted to give it 4 stars。 Ultimately couldn't because the author spent most of the book talking about issues and only really started to address ways to counteract in the conclusion。 That struck me as an academic "future work" approach。 Would have rather seen the author discuss in context throughout the book 。 I really wanted to give it 4 stars。 Ultimately couldn't because the author spent most of the book talking about issues and only really started to address ways to counteract in the conclusion。 That struck me as an academic "future work" approach。 Would have rather seen the author discuss in context throughout the book 。 。。。more

Petra ，

My problem with non-fiction books in general is that often I like the first third， but after that the same material gets regurgitated without adding much new to it。 That was the case with this book。 On top of that， it was a lot of complaining with no real suggestions for improvement。 It was a nice overview of how all the big data algorithms touch so many areas of our lives， but it could have been an article instead of a book。

Eliza ，

Truly a great read for any data scientist。 O'Neil carefully uncovers where big data + mathematical models are failing our communities in all aspects - models that predict recidivism rates are used to create unfair sentencing， how credit scores are being used in conjunction with opaque escores based on much wider swaths of personal data that have little to do with financial risk， how ads can microtarget individuals and create infinite permutations of realities we ingest from social media， and etc Truly a great read for any data scientist。 O'Neil carefully uncovers where big data + mathematical models are failing our communities in all aspects - models that predict recidivism rates are used to create unfair sentencing， how credit scores are being used in conjunction with opaque escores based on much wider swaths of personal data that have little to do with financial risk， how ads can microtarget individuals and create infinite permutations of realities we ingest from social media， and etc。 O'Neil also provides a framework for handling these concerns as well！ 。。。more

Angela Brooks ，

We are all being manipulated by social media and the internet。 Algorithms are unfair and not transparent。 They reinforce the systematic oppression that is plagueing our country。 Read this book！！

Melanie H ，

Promising premise， perfect title， and highly readable。 But if the topic of racist algorithms is in your wheelhouse， as it's in mine， you might not find anything new to pick at other than the scam that is the U。S。 News & World Report College rankings。 Promising premise， perfect title， and highly readable。 But if the topic of racist algorithms is in your wheelhouse， as it's in mine， you might not find anything new to pick at other than the scam that is the U。S。 News & World Report College rankings。 。。。more

Yuliyan Velichkov ，

Interesting book on the dark side of big data。 It is quite common that an algorithm doesn't work and there are also many ethical shortcomings with it。 The algorithms we created to be unbiased adapted our bias since we were the ones who made them Yet， big data has a great future upon it， and hopefully， people who are developing models based on it will make ethics a big part of the entire industry。 Interesting book on the dark side of big data。 It is quite common that an algorithm doesn't work and there are also many ethical shortcomings with it。 The algorithms we created to be unbiased adapted our bias since we were the ones who made them Yet， big data has a great future upon it， and hopefully， people who are developing models based on it will make ethics a big part of the entire industry。 。。。more

Assem Zhunis ，

Must read for data scientists， ML developers， and users of the black box models to better understand how ignorance of some ethical issues while building and applying the algorithms may transfer to unknown and self-feeding destructions。

Becky ，

You think this will be a nerdy math book， but it is very interesting。 We are all subject to a host of algorithms every day and these have real impacts on essentially every aspect of our private lives， many times adversely。 In addition these weapons of math destruction are mostly never transparent。 We need to hold these processes and the people who use them ethically and morally accountable。

David ，

Super interesting read。

Amy ，

Your enjoyment of this book will somewhat hinge on your familiarity with mathematics。 If you're a statistics and data person， there won't be enough specificity on how the numbers were crunched。 If you're not a mathematics person， you have to be happy with hearing example after example of how algorithms are sorting， judging， and probably hindering you; the injustice will be clear， but the how's and why's will elude you。 These things are happening， you'll know， but you can't do much more than shak Your enjoyment of this book will somewhat hinge on your familiarity with mathematics。 If you're a statistics and data person， there won't be enough specificity on how the numbers were crunched。 If you're not a mathematics person， you have to be happy with hearing example after example of how algorithms are sorting， judging， and probably hindering you; the injustice will be clear， but the how's and why's will elude you。 These things are happening， you'll know， but you can't do much more than shake your fist at the sky。 Math and I have never been on good terms。 I actively avoid it。 That said， I could understand every page of this book and was shocked at my level of interest。 I'm not sure I could ever love a book about data， but O'Neil writes in an accessible way that I appreciated。 Data-mining is a huge ethical concern， and we need to take it seriously。 。。。more

Matt Oreilly ，

Great book！ It doesn’t get much better than this for stats/data married to real world phenomena nerds。

Matthias Hernandez ，

An important book on the ethics of AI， albeit quite superficial。

Chris ，

8/10This book discusses the potential dangers and benefits of big data。 Algorithms are increasing determining many aspects of our lives and this week help teach what done of these issues are。 I wish we could address these issues before it really becomes too late

Travis Chambers ，

The idea of algorithms as "WMDs" was really interesting。 She had a couple of concrete examples， but I really wish there were more specifics。 The idea of algorithms as "WMDs" was really interesting。 She had a couple of concrete examples， but I really wish there were more specifics。 。。。more

AN ，

A good introduction to the dark side of some recent tech buzzwords and trends （Machine Learning， AI， etc。）。 What's in this book is not new if you have been in tech for a while。 But when you've been too deep in data and models， you probably need a quick touch/ a wake-up call like this book。 A good introduction to the dark side of some recent tech buzzwords and trends （Machine Learning， AI， etc。）。 What's in this book is not new if you have been in tech for a while。 But when you've been too deep in data and models， you probably need a quick touch/ a wake-up call like this book。 。。。more

Derek Henderson ，

Good overview of the dangers of big data and the models that feed on that data。 Some eye-opening examples。

Langdon Ogburn ，

Like reading a podcast。 Very sensational with very little room for nuance。

Ivan Valerdi ，

La autora explora las aplicaciones de los algoritmos matemáticos en diversos campos y como estos influyen en las vidas de las personas que se ven afectadas por los mismos: desde algoritmos que permiten *medir* tu desempeño académico para entrar a una universidad hasta algoritmos que pueden predecir tu historial delictivo （！！） con solo obtener un par de variables y generar un proxy。Bastante interesante para adentrarse en el mundo tech y sus aplicaciones en el mundo real。

Mysteryfan ，

Big Data codifies the past。 It does not invent the future。 This book documents the ways businesses and governments use algorithms in ways that harm。 Algorithms aren't neutral。 They reflect the biases of their coders and the data used。 It read a little like a laundry list， but still an important book。 Big Data codifies the past。 It does not invent the future。 This book documents the ways businesses and governments use algorithms in ways that harm。 Algorithms aren't neutral。 They reflect the biases of their coders and the data used。 It read a little like a laundry list， but still an important book。 。。。more

Jeffrey ，

I picked this up after hearing her talk on a podcast and her thoughts touch on many things I am working on and was hoping it would be helpful。 Unfortunately I don't think it will be for my purposes。 Has a lot of important and valid criticisms of the way we use data and algorithms to run many important aspects of our lives。 However， after introducing the main points it largely becomes a rant though and provided solutions are wishful thinking and very surface level。 I picked this up after hearing her talk on a podcast and her thoughts touch on many things I am working on and was hoping it would be helpful。 Unfortunately I don't think it will be for my purposes。 Has a lot of important and valid criticisms of the way we use data and algorithms to run many important aspects of our lives。 However， after introducing the main points it largely becomes a rant though and provided solutions are wishful thinking and very surface level。 。。。more

Misty ，

3。5 - really nothing new， but interesting and makes you think about the data we all rely upon。。。

Nilesh Makan ，

A must read for any mathematician or data scientist。 A thoughtfully written book that focuses on the perils of data analytics and how， when used in a profit centric way， can exacerbate iniquity and inequality。 Cathy O'Neil gives practical and real world examples of how data sciences can focus on areas that maximise profit at the detriment of society。 She also explains how， changing what we measure can improve the lives of those that need it the most。 A must read for any mathematician or data scientist。 A thoughtfully written book that focuses on the perils of data analytics and how， when used in a profit centric way， can exacerbate iniquity and inequality。 Cathy O'Neil gives practical and real world examples of how data sciences can focus on areas that maximise profit at the detriment of society。 She also explains how， changing what we measure can improve the lives of those that need it the most。 。。。more

ABHISHEK DAPTARDAR ，

Was expecting a more detailed review of how exactly are algorithms bad。this book takes a cursory glance over it's chapters and how individuals are suffering from it。 That system is true with anything really。 Policing is not perfect ， justice system is slow and has its share of issues and innocent bystanders。 but this case the author makes of how people's prejudices actually manifest in the complex algorithms and models 。 I was hoping for a more grey box if not a white box approach to dissecting Was expecting a more detailed review of how exactly are algorithms bad。this book takes a cursory glance over it's chapters and how individuals are suffering from it。 That system is true with anything really。 Policing is not perfect ， justice system is slow and has its share of issues and innocent bystanders。 but this case the author makes of how people's prejudices actually manifest in the complex algorithms and models 。 I was hoping for a more grey box if not a white box approach to dissecting this but all you get page after page is a cursory glance over with various examples how algorithms are flawed 。。。more

Reference

google book https://www.google.com/search?tbm=bks&q=Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

hathitrust digital library https://babel.hathitrust.org/cgi/ls?q1=Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy&field1=ocr&a=srchls&ft=ft&lmt=ft

The British Library https://bll01.primo.exlibrisgroup.com/discovery/search?query=any,contains,Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy&tab=LibraryCatalog&search_scope=Not_BL_Suppress&vid=44BL_INST:BLL01&lang=en&offset=0