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Weapons of Math Destruction

Cathy O'Neil, 2016

Why opaque models that score people at huge scale can wreck lives, from teachers fired by a formula to voters targeted by data, and how to disarm them.

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15 parts, about 23 minutes to read.

Why read it

  • Follows a fired Washington teacher to show how a black box score cannot be appealed and never learns whether firing her was right
  • Defines a harmful model by opacity, scale and damage, testing each against a recidivism questionnaire that asks prisoners about their first police contact
  • Closes with remedies: audit models as black boxes, drop data such as credit and acquaintances from recidivism scores, change laws, and aim models at helping

Who it is for

  • Teachers rated on student test scores, and administrators who rate them
  • Job seekers, borrowers and drivers wondering how hidden scores judge them
  • Data scientists and policymakers deciding how algorithms should be audited and regulated

What the summary covers

  1. From mathematics to the crash1 min
  2. A fired teacher in Washington2 min
  3. Healthy models and opinions in math1 min
  4. Race and recidivism scores2 min
  5. Inside the hedge fund1 min
  6. Ranking colleges2 min
  7. Predatory ads and for profit colleges2 min
  8. Policing by the map2 min
  9. Hiring filters2 min
  10. Scheduling software1 min
  11. Scoring teachers1 min
  12. Scores built from proxies2 min
  13. Old redlining in new forms1 min
  14. Facebook and political targeting1 min
  15. How the weapons compound, and how to disarm them2 min

Recommended by

  • Cory DoctorowScience fiction author and copyright reform activist
    Weapons of Math Destruction is a fantastic, plainspoken call to arms. It acknowledges that models aren’t going away: As a tool for identifying people in difficulty, they are amazing. But as a tool for punishing and disenfranchising, they’re a nightmare.
  • Ralph NaderLawyer and consumer protection activist
    Many algorithms are slaves to the inequalities of power and prejudice. If you don’t want these algorithms to become your masters, read Weapons of Math Destruction by Cathy O’Neil to deconstruct the latest growing tyranny of an arrogant establishment.
  • Yuval Noah HarariProfessor of history at the Hebrew University of Jerusalem
    A fascinating and deeply disturbing book.
  • Jordan EllenbergProfessor of mathematics at the University of Wisconsin Madison
    Cathy O’Neil has seen Big Data from the inside, and the picture isn’t pretty. Weapons of Math Destruction opens the curtain on algorithms that exploit people and distort the truth while posing as neutral mathematical tools. This book is wise, fierce, and desperately necessary.
  • Steven StrogatzProfessor of mathematics at Cornell University
    Even as a professional mathematician, I had no idea how insidious Big Data could be until I read Weapons of Math Destruction.
  • danah boydFounder of the Data and Society Research Institute
    In this fascinating account, Cathy O’Neil leverages her expertise in mathematics and her passion for social justice to poke holes in the triumphant narrative of Big Data. She makes a compelling case that math is being used to squeeze marginalized segments of society and magnify inequities. Her analysis is superb, her writing is enticing, and her findings are unsettling.
  • Astra TaylorDocumentary filmmaker and writer
    Combating secrecy with clarity and confusion with understanding, this book can help us change course before it’s too late.
  • Linda TiradoAuthor of the memoir Hand to Mouth
    From getting a job to finding a spouse, predictive algorithms are silently shaping and controlling our destinies. Cathy O’Neil takes us on a journey of outrage and wonder, with prose that makes you feel like it’s just a conversation. But it’s an important one. We need to reckon with technology.
  • Felix SalmonFinancial journalist and Slate Money host
    Next time you hear someone gushing uncritically about the wonders of Big Data, show them Weapons of Math Destruction. It’ll be salutary.

About the author

  • Cathy O'NeilMathematician and data scientist, founder of the algorithmic auditing firm ORCAA
    • Born in 1972, an American mathematician, data scientist and author.
    • Studied at UC Berkeley and received a PhD in mathematics from Harvard in 1999, then held positions in the mathematics departments of MIT and Barnard College.
    • Left academia in 2007 and worked for four years in finance, as an analyst at D. E. Shaw.
    • After leaving finance she became involved in Occupy Wall Street, in its Alternative Banking Group.
    • Launched the Lede Program for data journalism at Columbia University.
    • Her first book, Doing Data Science, was written with Rachel Schutt and published in 2013.
    • Founded ORCAA, an algorithmic auditing company, and sits on the board of the nonprofit OCEAN.
    • Won the Alice T. Schafer Prize of the Association for Women in Mathematics in 1993.

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