Verwandte Artikel zu Weapons of Math Destruction: How Big Data Increases...

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

 
9780451497338: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
  • VerlagCrown
  • Erscheinungsdatum2016
  • ISBN 10 0451497333
  • ISBN 13 9780451497338
  • EinbandTapa blanda
  • SpracheEnglisch
  • Anzahl der Seiten272

Gebraucht kaufen

Zustand: Befriedigend
Pages can have notes/highlighting...
Diesen Artikel anzeigen

Gratis für den Versand innerhalb von/der USA

Versandziele, Kosten & Dauer

EUR 29,28 für den Versand von Deutschland nach USA

Versandziele, Kosten & Dauer

Weitere beliebte Ausgaben desselben Titels

Suchergebnisse für Weapons of Math Destruction: How Big Data Increases...

Beispielbild für diese ISBN

O'Neil, Cathy
Verlag: Allen Lane, 1887
ISBN 10: 0451497333 ISBN 13: 9780451497338
Gebraucht Paperback

Anbieter: ThriftBooks-Dallas, Dallas, TX, USA

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.15. Bestandsnummer des Verkäufers G0451497333I3N00

Verkäufer kontaktieren

Gebraucht kaufen

EUR 11,29
Währung umrechnen
Versand: Gratis
Innerhalb der USA
Versandziele, Kosten & Dauer

Anzahl: 1 verfügbar

In den Warenkorb

Foto des Verkäufers

Cathy O'Neil
ISBN 10: 0451497333 ISBN 13: 9780451497338
Neu Taschenbuch

Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Taschenbuch. Zustand: Neu. Neu Neuware, Importqualität, auf Lager - Longlisted for the National Book AwardNew York Times Bestseller 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 car loan, how much we pay for health 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. But as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination: If a poor student can't get a loan because a lending model deems him too risky (by virtue of his zip code), he's then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a 'toxic cocktail for democracy.' Welcome to the dark side of Big Data. 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 résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. O'Neil calls on modelers 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. - Longlist for National Book Award (Non-Fiction) - Goodreads, semi-finalist for the 2016 Goodreads Choice Awards (Science and Technology) - Kirkus, Best Books of 2016 - New York Times, 100 Notable Books of 2016 (Non-Fiction) - The Guardian, Best Books of 2016 - WBUR's 'On Point,' Best Books of 2016: Staff Picks - Boston Globe, Best Books of 2016, Non-Fiction. Bestandsnummer des Verkäufers INF1000537669

Verkäufer kontaktieren

Neu kaufen

EUR 29,99
Währung umrechnen
Versand: EUR 29,28
Von Deutschland nach USA
Versandziele, Kosten & Dauer

Anzahl: 2 verfügbar

In den Warenkorb