We live in the age of the algorithm. Increasingly, the decisions that affect our lives - whether we get a job or a loan, how much we pay for insurance - are being made by mathematical models. In theory, this should lead to greater fairness: everyone is judged according to the same rules, and bias is eliminated
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 O'Neil's book offers a frightening look at how algorithms are increasingly regulating people... Her knowledge of the power and risks of mathematical models, coupled with a gift for analogy, makes her one of the most valuable observers of the continuing weaponization of big data... [She] does a masterly job explaining the pervasiveness and risks of the algorithms that regulate our lives New York Times Book Review 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 Even as a professional mathematician, I had no idea how insidious Big Data could be until I read Weapons of Math Destruction. Though terrifying, it's a surprisingly fun read: O'Neil's vision of a world run by algorithms is laced with dark humour and exasperation - like a modern-day Dr Strangelove or Catch-22. It is eye-opening, disturbing, and deeply important -- Steven Strogatz, Cornell University, author of The Joy of x 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 In this fascinating account, Cathy O'Neil leverages her expertise in mathematics and her passion for social justice to poke holes in the triumphant narrative of Big Data. She makes a compelling case that math is being used to squeeze marginalized segments of society and magnify inequities. Her analysis is superb, her writing is enticing, and her findings are unsettling -- danah boyd, founder of Data & Society and author of It's Complicated Next time you hear someone gushing uncritically about the wonders of Big Data, show them Weapons of Math Destruction. It'll be salutary -- Felix Salmon, Fusion Weapons of Math Destruction is the Big Data story Silicon Valley proponents won't tell... [It] pithily exposes flaws in how information is used to assess everything from creditworthiness to policing tactics... A thought-provoking read for anyone inclined to believe that data doesn't lie Reuters 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 If you've ever suspected there was something baleful about our deep trust in data, but lacked the mathematical skills to figure out exactly what it was, this is the book for you Salon
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.