Weapons of Math Destruction

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* Book: Cathy O’Neil. Weapons of Math Destruction: big data increases inequality and threatens democracy. Crown Random House, 2016

URL = https://weaponsofmathdestructionbook.com/


"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."


Evelyn Lamb:

"Weapons of math destruction, which O’Neil refers to throughout the book as WMDs, are mathematical models or algorithms that claim to quantify important traits: teacher quality, recidivism risk, creditworthiness but have harmful outcomes and often reinforce inequality, keeping the poor poor and the rich rich. They have three things in common: opacity, scale, and damage. They are often proprietary or otherwise shielded from prying eyes, so they have the effect of being a black box. They affect large numbers of people, increasing the chances that they get it wrong for some of them. And they have a negative effect on people, perhaps by encoding racism or other biases into an algorithm or enabling predatory companies to advertise selectively to vulnerable people, or even by causing a global financial crisis.

O’Neil is an ideal person to write this book. She is an academic mathematician turned Wall Street quant turned data scientist who has been involved in Occupy Wall Street and recently started an algorithmic auditing company. She is one of the strongest voices speaking out for limiting the ways we allow algorithms to influence our lives and against the notion that an algorithm, because it is implemented by an unemotional machine, cannot perpetrate bias or injustice.


O’Neil talks about financial WMDs and her experiences , but the examples in her book come from many other facets of life as well: college rankings, employment application screeners, policing and sentencing algorithms, workplace wellness programs, and the many inappropriate ways credit scores reward the rich and punish the poor. As an example of the latter, she shares the galling statistic that “in Florida, adults with clean driving records and poor credit scores paid an average of $1552 more than the same drivers with excellent credit and a drunk driving conviction.” (Emphasis hers.)

She shares stories of people who have been deemed unworthy in some way by an algorithm. There’s the highly-regarded teacher who is fired due to a low score on a teacher assessment tool, the college student who couldn’t get a minimum wage job at a grocery store due to his answers on a personality test, the people whose credit card spending limits were lowered because they shopped at certain stores. To add insult to injury, the algorithms that judged them are completely opaque and unassailable. People often have no recourse when the algorithm makes a mistake.

Many WMDs create feedback loops that perpetuate injustice. Recidivism models and predictive policing algorithms—programs that send officers to patrol certain locations based on crime data—are rife with the potential for harmful feedback loops.


While Weapons of Math Destruction is full of hard truths and grim statistics, it is also accessible and even entertaining. O’Neil’s writing is direct and easy to read—I devoured it in an afternoon. And the book is not all grim. In the last chapter, she shares some ideas of how we can disarm WMDs and use big data for good. She proposes a Hippocratic Oath for data scientists and writes about how to regulate math models. Let’s return to the sentences I opened with: “The technology already exists. It’s only the will we’re lacking.” That is bleak—we aren’t doing what we can—but should give us some hope as well. The technology exists! If we develop the will, we can use big data to advance equality and justice." (https://blogs.scientificamerican.com/roots-of-unity/review-weapons-of-math-destruction/)

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