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

O'Neil, Cathy

Couverture souple

ISBN 10 : 0241296811 ISBN 13 : 9780241296813

Editeur : Allen Lane, 2016

Synopsis

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.

A propos du livre

À propos de l?auteur

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.

À propos de la quatrième de couverture

'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.

Détails de l'édition

• Éditeur: Allen Lane
• Date d'édition: 2016
• Langue: anglais
• ISBN 10: 0241296811
• ISBN 13: 9780241296813
• Reliure: Broché
• Nombre de pages: 272
• Coordonnées du fabricant: non disponible