kdaedwards's review against another edition
3.0
I appreciated reading a book that states a lot of the same ideas as I've had over the years about misuse of data and information. However, I would have preferred a book that was a bit more technical and had less simplifying of concepts to reach a wider audience; some of the simplification actually were misleading, especially given the true nuance of the points discussed.
kstring's review against another edition
3.0
This book is SO repetitive. Listen: I got the message after Chapter 1, no need to write a whole book. If I wasn't reading for a class, I might have stopped after Chapter 3. Granted, keeping these ideas in mind while coding up machine learning algorithms is very important so hearing case studies was helpful for me. Some of the stories were interesting to hear though because I never heard about them in the news and I didn't realize how many determining processes we run by algorithm these days. However, I would like if she presented more solutions to these problems rather than discuss the problems themselves; at one point it sounds like O'Neil wished she could go around and solve all these problems herself, but what if I want to help her? How would I do that? What are things the average consumer can do to fight back?
The biggest thing that bothered me with this book was how she kept saying "We'll talk about this later", even when there was only 30 minutes left in the audiobook!
I can't decide a genre for this book. Is it vent sesh? A social commentary? A biography? The beginning is heavy biography, the middle is a vent sesh, and the end is largely social commentary.
The biggest thing that bothered me with this book was how she kept saying "We'll talk about this later", even when there was only 30 minutes left in the audiobook!
I can't decide a genre for this book. Is it vent sesh? A social commentary? A biography? The beginning is heavy biography, the middle is a vent sesh, and the end is largely social commentary.
lindy_b's review against another edition
3.0
Weapons of Math Destruction is a quick read about the social impacts of mathematical modeling. O'Neil's writing is engaging and you don't really have to know anything about math/statistics to understand it.
With accessibility there's a loss of detail, though. It's all very surface level, and most of the case studies will probably be familiar to you if you have read tech news with any regularity within the last five years.
At one point O'Neil quips that political speeches are often boring because they're trying to be appealing to basically everyone, and that is how I feel about this book. O'Neil shies away from the logical conclusion (critique of capitalism, because capitalism necessitates the sort of inequalities driven by WMDs that O'Neil calls out in the text). The conclusions she does offer up aren't all that well-formed or compelling.
With accessibility there's a loss of detail, though. It's all very surface level, and most of the case studies will probably be familiar to you if you have read tech news with any regularity within the last five years.
At one point O'Neil quips that political speeches are often boring because they're trying to be appealing to basically everyone, and that is how I feel about this book. O'Neil shies away from the logical conclusion (critique of capitalism, because capitalism necessitates the sort of inequalities driven by WMDs that O'Neil calls out in the text). The conclusions she does offer up aren't all that well-formed or compelling.
paigeol's review against another edition
3.5
Very reductive and lacked evidence to support claims. However, it did make important points about opacity in algorithms and other implications. Not it’s fault but was dated since it was published in 2016
jgstewart87's review against another edition
1.0
This book has very little to do with big data and algorithms. Instead, it's typical 'Occupy Wall Street' messaging wrapped up in language pretending to be about something else to reach a wider audience. It's as though the OWS movement fell apart and they needed a new vehicle to deliver their creed.
It's as though the author mainly wanted to criticize capitalism, but lacked the wherewithall to outright do it. So instead, she focuses on the tools used by the system, criticizing them as though they are somehow to blame for everything, not recognizing they are exentsions of the system.
She makes some egregious claims as well. She outright accuses the rating agencies of fraud, nearly reduces the entire college pricing schemes to algorithms, blames China's culture of cheating on algorithms, which is mostly simple reductions of complex issues into simplified explanations. Which, funnily enough, is exactly what she complains about algorithms doing.
She does raise a few good points about the use of proxies or the lack of feedback loops, but they're lost amidst the calling for bankers to be jailed, whining about companies seeking higher profits, or lamenting about marketing being the sleezy industry that it is. None of it has anything to do with algorithms or big data, they're only the tools that help bad people be bad.
It's as though the author mainly wanted to criticize capitalism, but lacked the wherewithall to outright do it. So instead, she focuses on the tools used by the system, criticizing them as though they are somehow to blame for everything, not recognizing they are exentsions of the system.
She makes some egregious claims as well. She outright accuses the rating agencies of fraud, nearly reduces the entire college pricing schemes to algorithms, blames China's culture of cheating on algorithms, which is mostly simple reductions of complex issues into simplified explanations. Which, funnily enough, is exactly what she complains about algorithms doing.
She does raise a few good points about the use of proxies or the lack of feedback loops, but they're lost amidst the calling for bankers to be jailed, whining about companies seeking higher profits, or lamenting about marketing being the sleezy industry that it is. None of it has anything to do with algorithms or big data, they're only the tools that help bad people be bad.
robdabear's review against another edition
3.0
I strongly dislike books that use emotional appeal to highlight prevalent social justice issues. That's why I was attracted to this book. If someone could show that 'big data' is a driving factor in the social injustices of today's world, what more compelling argument can you find? This is largely the case in Weapons of Math Destruction, and for that I was intrigued. But as the book progressed, I was disappointed that it seemed to devolve toward suiting the ideological preferences of the author, and the conclusion left me with the bitter taste that this was just someone else trying to tell me what to think.
All of that said, I enjoyed the book, I really did, and I find the arguments sound and compelling. This should be required reading for data scientists and those who use the tools described in this book on a daily basis. It's the social justice tone I took issue with, but perhaps that just means I'm a grouch who should stick to reading textbooks instead. Put my own personal biases aside, this is a good 3.5 from me.
All of that said, I enjoyed the book, I really did, and I find the arguments sound and compelling. This should be required reading for data scientists and those who use the tools described in this book on a daily basis. It's the social justice tone I took issue with, but perhaps that just means I'm a grouch who should stick to reading textbooks instead. Put my own personal biases aside, this is a good 3.5 from me.