#2682
Mentioned in 14 episodes

Weapons of Math Destruction

How Big Data Increases Inequality and Threatens Democracy
Book • 2016
In this book, Cathy O'Neil explores the societal impact of big data algorithms, which she terms 'Weapons of Math Destruction' (WMDs).

These algorithms are used in various fields such as insurance, advertising, education, and policing, and they often reinforce discrimination, amplify inequality, and harm the poor.

O'Neil argues that these models are opaque, unregulated, and difficult to contest, leading to a 'toxic cocktail for democracy'.

She calls for greater responsibility from modelers and policymakers to regulate these algorithms and for the public to become more aware of their impact.

The book provides numerous examples of how these models can go wrong and emphasizes the need for ethical considerations in the use of big data models.

Mentioned by

Mentioned in 14 episodes

Mentioned by
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Michael Lewis
, highlighting its discussion on how math is used to disguise exploitation.
372 snips
Episode 10: “Anybody Can Win, but Everybody’s Gonna Lose”
Recommended by
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Jess Ramos
as a book that changed her perspective on data and its societal impact.
60 snips
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Mentioned by
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Hannah Fry
as a book highlighting the biases and catastrophic consequences of algorithms in society.
57 snips
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Mentionné par
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Justine Cassell
comme un livre qui démontre comment les algorithmes peuvent reproduire des biais.
31 snips
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Mentioned by Kathy O'Neill, a mathematician, data scientist, and author, as a catchy and terrifying name for a book about the dangers of algorithms.
30 snips
870: Personality Tests | Skeptical Sunday
Mentioned by
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Charles Humble
to emphasize that the issues discussed in the book are still relevant today.
22 snips
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Mentioned by
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Kris Brandow
when discussing AI algorithms and inherent bias.
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Recommended by
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Jill Nephew
as a book providing simple examples of destructive algorithms.
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Recommended by
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Andrew Hart
as an insightful read on AI and mathematical models.
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Mentioned by
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Dr. David Cox
to emphasize how simple models can inflict societal harm.
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Mencionado por
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Alexey Villas-Bôas
ao discutir ciclos de retroalimentação em decisões tomadas com IA, referenciando o trabalho de Cathy O'Neill.
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Sophia Rowland
as a primer on understanding the risks of AI.
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Mentioned by
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Brian Christian
as a book addressing ethical concerns within the machine learning community.
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Mentioned as an example of a popular math book that uses stories to tie together complex topics.
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Nevnt av
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Audun Kvitland Røstad
som en bok som sier at algoritmer er meninger bakt inn i matematikk.
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Citato da
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Matteo Flora
come esempio di "math washing" e di come non si possa dare la colpa a Chat GPT.
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Mentioned by
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David Shapiro
as relevant to the discussion on AI safety and ethics.
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Mentioned by
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Andrew Keen
in relation to the discussion on the dark side of technology.
Episode 2300: Sandra Matz makes the Case for a Data-Driven Science of Predicting and Changing Human Behavior
Mentioned by
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Kate Strachnyi
in the context of her book Mothers of Data Science.
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