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2007-2008 Financial Crisis
Big Data, or the so-called “Big Data economy,” is a field that seeks to analyze incredibly large data sets through models and algorithms—some of which O’Neil classifies as “weapons of math destruction” because of how harmful they can be on a societal level. More colloquially, the idea of “big data” refers to our modern era, in which data about us—where we live, what we shop for, how much money we make, and more—is, in turn, used to control important aspects of our lives. Big Data can impact how we’re targeted by advertisers and politicians; whether we can secure loans, credit, or insurance policies; and more.
Big Data Quotes in Weapons of Math Destruction
The Weapons of Math Destruction quotes below are all either spoken by Big Data or refer to Big Data. For each quote, you can also see the other terms and themes related to it (each theme is indicated by its own dot and icon, like this one:
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Introduction
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The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.
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Chapter 6: Ineligible to Serve
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The hiring business is automating, and many of the new programs include personality tests like the one Kyle Behm took. It is now a $500 million annual business and is growing by 10 to 15 percent a year […]. Such tests now are used on 60 to 70 percent of prospective workers in the United States […].
Naturally, these hiring programs can't incorporate information about how the candidate would actually perform at the company. That’s in the future, and therefore unknown. So like many other Big Data programs, they settle for proxies. And as we’ve seen, proxies are bound to be inexact and often unfair.
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Phrenology was a model that relied on pseudoscientific nonsense to make authoritative pronouncements, and for decades it went untested. Big Data can fall into the same trap. Models like the ones that red-lighted Kyle Behm and blackballed foreign medical students at St. George’s can lock people out, even when the “science” inside them is little more than a bundle of untested assumptions.
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Chapter 7: Sweating Bullets
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With Big Data, […] businesses can now analyze customer traffic to calculate exactly how many employees they will need each hour of the day. The goal, of course, is to spend as little money as possible, which means keeping staffing at the bare minimum while making sure that reinforcements are on hand for the busy times.
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While its scores are meaningless, the impact of value-added modeling is pervasive and nefarious. “I’ve seen some great teachers convince themselves that they were mediocre at best based on those scores,” Clifford said. “It moved them away from the great lessons they used to teach, toward increasing test prep. To a young teacher, a poor value-added score is punishing, and a good one may lead to a false sense of accomplishment that has not been earned.”
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Conclusion
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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.
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Big Data Term Timeline in Weapons of Math Destruction
The timeline below shows where the term Big Data appears in Weapons of Math Destruction. The colored dots and icons indicate which themes are associated with that appearance.
Introduction
...human’s potential as “students, workers, lovers, criminals.” This, O’Neil writes, is now known as the Big Data economy.
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But around 2010, as Big Data saw mathematics involving itself in human affairs like never before, O’Neil began to feel troubled....
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...announces her intent to take her readers on a tour of “the dark side of Big Data ” and examine the injustices that WMDs cause as they control most aspects of modern...
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Chapter 2: Shell Shocked
...As she adjusted to her new job, she found lots of parallels between finance and Big Data —the biggest of which was that in both fields, money and self-worth were inextricably interwoven....
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Chapter 4: Propaganda Machine
...alleges that the internet isn’t the equalizing, democratizing force it promised to be. Instead, as Big Data and tech companies have learned more about individual users, they’ve created rankings and categorizations of...
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Chapter 5: Civilian Casualties
...The software was called PredPol (short for “predictive policing”), and it was made by a Big Data start-up based in California. The software promised to use historical crime data to show, hour...
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Chapter 6: Ineligible to Serve
...O’Neil asserts, are prone to confirmation biases rooted in “pseudoscientific nonsense.” In this way, modern-day Big Data is a lot like phrenology—the racist and long-debunked study of whether irregularities of the human...
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Chapter 7: Sweating Bullets
Irregular schedules and clopenings are both products of the Big Data economy. WMDs that treat workers “like cogs in a machine” create these trends and entrench...
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Chapter 8: Collateral Damage
...faulty algorithms. These automatic systems now need a human hand to sift through their mistakes. Big Data needs to slow down and allow humans to play a greater role in sorting sensitive...
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...get, and what their interest rate will be. People are trading privacy for discounts—and if Big Data algorithms find something as minor as a spelling error in a mountain of data about...
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Chapter 9: No Safe Zone
In the age of Big Data , insurers can judge us by how we drive in entirely new ways. In 2015,...
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Chapter 10: The Targeted Citizen
Nowadays, Big Data has given politicians lots of powerful tools for targeting “micro-groups” of citizens for votes and...
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Conclusion
...systems are stuck in time—engineers have to change them as society progresses. So essentially, “ Big Data processes codify the past” rather than inventing the future. Only humans have the “moral imagination”...
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But Big Data , O’Neil asserts, should be disruptive when it comes to things that actually matter, like...
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...of a “new revolution”—and learn to bring transparency, fairness, and accountability to the age of Big Data .
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Afterword
...things from different points of view—and even mathematical proofs can be full of mistakes. But Big Data has a duty to clarify the noise—not contribute to it even more. Big tech companies...
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