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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Paperback – 6 Jul 2017
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Fascinating and deeply disturbing (Yuval Noah Harari Guardian Books of the Year)
This is a manual for the 21st-century citizen, and it succeeds where other big data accounts have failed - it is accessible, refreshingly critical and feels relevant and urgent (Federica Cocco Financial Times)
Well-written, entertaining and very valuable (Danny Dorling Times Higher Education)
O'Neil has become a whistle-blower for the world of Big Data... Her work makes particularly disturbing points about how being on the wrong side of an algorithmic decision can snowball in incredibly destructive ways (Time)
Cathy O'Neil has seen Big Data from the inside, and the picture isn't pretty. 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, author of How Not To Be Wrong)
Weapons of Math Destruction is a fantastic, plainspoken call to arms. Cathy O'Neil's book is important precisely because she believes in data science. It's a vital crash course in why we must interrogate the systems around us and demand better (Cory Doctorow, author of Little Brother and co-editor of Boing Boing)
Often we don't even know where to look for those important algorithms, because by definition the most dangerous ones are also the most secretive. That's why the catalogue of case studies in O'Neil's book are so important; she's telling us where to look (Guardian)
In today's world, if you want to change your fate you've got to pray at the altar of the algorithm... As math guru Cathy O'Neil argues in her newest book, these models are just the latest way America's institutions perpetuate bias and prejudice to reward the rich and keep the poor, well, poor. It's a nuanced reminder that big data is only as good as the people wielding it (Wired)
Not math heavy, but written in an exceedingly accessible, almost literary style; her fascinating case studies of WMDs fit neatly into the genre of dystopian literature. There's a little Philip K. Dick, a little Orwell, a little Kafka in her portrait of powerful bureaucracies ceding control of the most intimate decisions of our lives to hyper-empowered computer models riddled with all of our unresolved, atavistic human biases (Chris Jackson Paris Review)
O'Neil is an ideal person to write this book... She is one of the strongest voices speaking out for limiting the ways we allow algorithms to influence our lives and against the notion that an algorithm, because it is implemented by an unemotional machine, cannot perpetrate bias or injustice... While Weapons of Math Destruction is full of hard truths and grim statistics, it is also accessible and even entertaining. O'Neil's writing is direct and easy to read - I devoured it in an afternoon (Evelyn Lamb Scientific American)
About the Author
Cathy O'Neil is a data scientist and author of the blog mathbabe.org. She earned a Ph.D. in mathematics from Harvard and taught at Barnard College before moving to the private sector, where she worked for the hedge fund D. E. Shaw. She then worked as a data scientist at various start-ups, building models that predict people's purchases and clicks. O'Neil started the Lede Program in Data Journalism at Columbia and is the author of Doing Data Science. She appears weekly on the Slate Money podcast.
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The author passion for the subject can be felt through the book and although enjoyed it did leave me feeling a little saddened to see so many examples of poorly implemented analytics.
The book would have benefitted from a broader perspective and also included success stories to show how data can also be a force for good to simplify and improve our lives. At the end of the day these are tools and it’s how people use them that can produce these WMD’s.
Also reminds us of the need to educate the wider community on how data is used and that data asset needs to benefit everyone.
The book covers several different fields, all of them tied by the common theme of how the lack of understanding of algorithms, sometimes unintentionally, sometimes for personal gain, leads to entirely false evaluations of phenomena, based on spurious correlations or too simplistic approaches, that quite too often provide false bases on top of which important, but entirely unsubstantiated decisions, are already being made.
I strongly advise anyone, but in particular people working in IT, to read this book since it is important that more people are made aware of how little their choices, and who they really are, may matter in their lives, once their data is fed to the algorithms most companies use nowadays.
IT people can, and should, use this knowledge to push for a review of how people data is used to make decisions that affect people's lives.