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Weapo*s of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil

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Here are the product details for "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy" by Cathy O'Neil:

- ISBN: 9780141985411

- Author: Cathy O'Neil

- Genre: Econometrics & Statistics

- Publication: Penguin

- Pages: 272 pages

- Format: Paperback

- Language: English

**Book Description:**

In "Weapons of Math Destruction," Cathy O'Neil addresses the impact of mathematical models on modern life and how they can exacerbate inequality and threaten democratic values. This New York Times bestseller serves as a wake-up call regarding the pervasive use of algorithms and mathematical models in decision-making processes that affect various aspects of our lives.

O'Neil, an advocate for algorithmic accountability, sheds light on the fact that many critical decisions, such as educational opportunities, loan approvals, insurance pricing, and more, are increasingly made not by humans but by mathematical models. In theory, this reliance on algorithms should promote fairness and eliminate bias. However, O'Neil argues that the opposite is happening.

The models being employed today are often opaque, unregulated, and resistant to challenge, even when they produce incorrect results. Most alarmingly, they can reinforce existing forms of discrimination. O'Neil takes readers on a journey through the impact of these "weapons of math destruction" on individuals and society. These models are used to evaluate teachers and students, screen job applications, determine loan eligibility, assess employee performance, target political campaigns, and even monitor health.

O'Neil calls for greater accountability among those who create and use these algorithms, as well as for policymakers to enact regulations governing their use. Ultimately, she empowers readers to become more informed and critical consumers of the models that shape our lives, encouraging them to ask tough questions, seek the truth, and advocate for change.

This book is highly relevant for anyone interested in understanding the societal implications of data-driven decision-making and the need for transparency and fairness in algorithmic systems.

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