Weapons of Math Destruction
How Big Data Increases Inequality and Threatens Democracy
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4.4 • 13 Ratings
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- £4.99
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- £4.99
Publisher Description
'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times
'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year
In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, 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.
PUBLISHERS WEEKLY
This taut and accessible volume, the stuff of technophobes' nightmares, explores the myriad ways in which large-scale data modeling has made the world a less just and equal place. O'Neil speaks from a place of authority on the subject: a Barnard professor turned Wall Street quant, she renounced the latter profession after the 2008 market collapse and decided to educate laypeople. Unlike some other recent books about data collection, hers is not hysterical; she offers more of a chilly wake-up call as she walks readers through the ways the "big data" industry has facilitated social ills such as skyrocketing college tuitions, policing based on racial profiling, and high unemployment rates in vulnerable communities. She also homes in on the ways these systems are frequently destructive even to the privileged: sloppy data-gathering companies misidentify people and flag them as criminals, and algorithms determine employee value during company-wide firings. The final chapter, in which O'Neil discusses Facebook's increasing electoral influence, feels eerily prescient. She offers no one easy solution, but has several reasonable suggestions as to how the future can be made more equitable and transparent for all.
Customer Reviews
Eye opening
Excellent book, showcasing the subjectivity present within statistical models, which is seldom conscious in today’s big data world.
My only grief with the book is the political sentiment haunting the text. The authors logic seems something as such:
1. Model A requires parameter B as a pass criteria.
2. Parameter B is not commonly found in people C.
3. Therefore model A discriminates against people C.
(In this example, one could take A as ‘University Acceptance’, B as ‘high SAT scores’ and C as ‘immigrants’, using some of the book’s topics as an example).
This logic is faulty. It wags the ‘prejudice’ finger at the model which makes no reference to any person, and in fact would accept any person C if possessing parameter B. The model merely judges skill and not the origin of the person.
Instead, one should take a step back and blame the *cause* for people C having less of B (e.g. lack of schooling resources in poorer zip codes). That is the real problem. And in a world optimised as such, the model would not be the pernicious, negative feedback looping, WMD that the author might curiously label it as.
I thoroughly recommend the book to anyone looking to have insight into how data your data may (or most likely *is*) being currently used to assess you. This should apply to everyone!
When life-changing code goes bad
For the common reader, this book is a fascinating dive into the mathematical mechanisms that govern the modern world - how they misfire, where they can be improved, and where they’re headed next.