Cathy O'Neil


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The United States
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Catherine ("Cathy") Helen O'Neil is an American mathematician and the author of the blog mathbabe.org and several books on data science, including Weapons of Math Destruction. She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism, Tow Center and was employed as Data Science Consultant at Johnson Research Labs.

She lives in New York City and is active in the Occupy movement.

Average rating: 3.87 · 16,724 ratings · 2,233 reviews · 6 distinct worksSimilar authors
Weapons of Math Destruction...

3.87 avg rating — 16,048 ratings — published 2016 — 36 editions
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On Being a Data Skeptic

3.81 avg rating — 158 ratings — published 2013 — 4 editions
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Armas de destrucción matemá...

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4.67 avg rating — 3 ratings
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Doing Data Science

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3.69 avg rating — 511 ratings — published 2013 — 10 editions
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Occupy Finance

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3.88 avg rating — 8 ratings — published 2013
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Once and Future Feminist (B...

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3.75 avg rating — 16 ratings2 editions
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“Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide. We have to explicitly embed better values into our algorithms, creating Big Data models that follow our ethical lead. Sometimes that will mean putting fairness ahead of profit.”
Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

“Here we see that models, despite their reputation for impartiality, reflect goals and ideology. When I removed the possibility of eating Pop-Tarts at every meal, I was imposing my ideology on the meals model. It’s something we do without a second thought. Our own values and desires influence our choices, from the data we choose to collect to the questions we ask. Models are opinions embedded in mathematics.”
Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

“Racism, at the individual level, can be seen as a predictive model whirring away in billions of human minds around the world. It is built from faulty, incomplete, or generalized data. Whether it comes from experience or hearsay, the data indicates that certain types of people have behaved badly. That generates a binary prediction that all people of that race will behave that same way. Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias.”
Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

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