Download PDF Weapons of Math Destruction How Big Data Increases Inequality and Threatens Democracy Audible Audio Edition Cathy O'Neil Random House Audio Books
Download As PDF : Weapons of Math Destruction How Big Data Increases Inequality and Threatens Democracy Audible Audio Edition Cathy O'Neil Random House Audio Books
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 troublingly, 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 paroles, 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 savvier about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.
Download PDF Weapons of Math Destruction How Big Data Increases Inequality and Threatens Democracy Audible Audio Edition Cathy O'Neil Random House Audio Books
"So here you are on Amazon's web page, reading about Cathy O'Neil's new book, Weapons of Math Destruction. Amazon hopes you buy the book (and so do I, it's great!). But Amazon also hopes it can sell you some other books while you're here. That's why, in a prominent place on the page, you see a section entitled:
Customers Who Bought This Item Also Bought
This section is Amazon's way of using what it knows -- which book you're looking at, and sales data collected across all its customers -- to recommend other books that you might be interested in. It's a very simple, and successful, example of a predictive model: data goes in, some computation happens, a prediction comes out. What makes this a good model? Here are a few things:
1. It uses relevant input data.The goal is to get people to buy books, and the input to the model is what books people buy. You can't expect to get much more relevant than that.
2. It's transparent. You know exactly why the site is showing you these particular books, and if the system recommends a book you didn't expect, you have a pretty good idea why. That means you can make an informed decision about whether or not to trust the recommendation.
3. There's a clear measure of success and an embedded feedback mechanism. Amazon wants to sell books. The model succeeds if people click on the books they're shown, and, ultimately, if they buy more books, both of which are easy to measure. If clicks on or sales of related items go down, Amazon will know, and can investigate and adjust the model accordingly.
Weapons of Math Destruction reviews, in an accessible, non-technical way, what makes models effective -- or not. The emphasis, as you might guess from the title, is on models with problems. The book highlights many important ideas; here are just a few:
1. Models are more than just math. Take a look at Amazon's model above: while there are calculations (simple ones) embedded, it's people who decide what data to use, how to use it, and how to measure success. Math is not a final arbiter, but a tool to express, in a scalable (i.e., computable) way, the values that people explicitly decide to emphasize. Cathy says that "models are opinions expressed in mathematics" (or computer code). She highlights that when we evaluate teachers based on students' test scores, or assess someone's insurability as a driver based on their credit record, we are expressing opinions: that a successful teacher should boost test scores, or that responsible bill-payers are more likely to be responsible drivers.
2. Replacing what you really care about with what you can easily get your hands on can get you in trouble. In Amazon's recommendation model, we want to predict book sales, and we can use book sales as inputs; that's a good thing. But what if you can't directly measure what you're interested in? In the early 1980's, the magazine US News wanted to report on college quality. Unable to measure quality directly, the magazine built a model based on proxies, primarily outward markers of success, like selectivity and alumni giving. Predictably, college administrators, eager to boost their ratings, focused on these markers rather than on education quality itself. For example, to boost selectivity, they encouraged more students, even unqualified ones, to apply. This is an example of gaming the model.
3. Historical data is stuck in the past. Typically, predictive models use past history to predict future behavior. This can be problematic when part of the intention of the model is to break with the past. To take a very simple example, imagine that Cathy is about to publish a sequel to Weapons of Math Destruction. If Amazon uses only purchase data, the Customers Who Bought This Also Bought list would completely miss the connection between the original and the sequel. This means that if we don't want the future to look just like the past, our models need to use more than just history as inputs. A chapter about predictive models in hiring is largely devoted to this idea. A company may think that its past, subjective hiring system overlooks qualified candidates, but if it replaces the HR department with a model that sifts through resumes based only on the records of past hires, it may just be codifying (pun intended) past practice. A related idea is that, in this case, rather than adding objectivity, the model becomes a shield that hides discrimination. This takes us back to Models are more than just math and also leads to the next point:
4. Transparency matters! If a book you didn't expect shows up on The Customers Who Bought This Also Bought list, it's pretty easy for Amazon to check if it really belongs there. The model is pretty easy to understand and audit, which builds confidence and also decreases the likelihood that it gets used to obfuscate. An example of a very different story is the value added model for teachers, which evaluates teachers through their students' standardized test scores. Among its other drawbacks, this model is especially opaque in practice, both because of its complexity and because many implementations are built by outsiders. Models need to be openly assessed for effectiveness, and when teachers receive bad scores without knowing why, or when a single teacher's score fluctuates dramatically from year to year without explanation, it's hard to have any faith in the process.
5. Models don't just measure reality, but sometimes amplify it, or create their own. Put another way, models of human behavior create feedback loops, often becoming self-fulfilling prophecies. There are many examples of this in the book, especially focusing on how models can amplify economic inequality. To take one example, a company in the center of town might notice that workers with longer commutes tend to turn over more frequently, and adjust its hiring model to focus on job candidates who can afford to live in town. This makes it easier for wealthier candidates to find jobs than poorer ones, and perpetuates a cycle of inequality. There are many other examples: predictive policing, prison sentences based on recidivism, e-scores for credit. Cathy talks about a trade-off between efficiency and fairness, and, as you can again guess from the title, argues for fairness as an explicit value in modeling.
Weapons of Math Destruction is not a math book, and it is not investigative journalism. It is short -- you can read it in an afternoon -- and it doesn't have time or space for either detailed data analysis (there are no formulas or graphs) or complete histories of the models she considers. Instead, Cathy sketches out the models quickly, perhaps with an individual anecdote or two thrown in, so she can get to the main point -- getting people, especially non-technical people, used to questioning models. As more and more aspects of our lives fall under the purview of automated data analysis, that's a hugely important undertaking."
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Weapons of Math Destruction How Big Data Increases Inequality and Threatens Democracy Audible Audio Edition Cathy O'Neil Random House Audio Books Reviews :
Weapons of Math Destruction How Big Data Increases Inequality and Threatens Democracy Audible Audio Edition Cathy O'Neil Random House Audio Books Reviews
- So here you are on 's web page, reading about Cathy O'Neil's new book, Weapons of Math Destruction. hopes you buy the book (and so do I, it's great!). But also hopes it can sell you some other books while you're here. That's why, in a prominent place on the page, you see a section entitled
Customers Who Bought This Item Also Bought
This section is 's way of using what it knows -- which book you're looking at, and sales data collected across all its customers -- to recommend other books that you might be interested in. It's a very simple, and successful, example of a predictive model data goes in, some computation happens, a prediction comes out. What makes this a good model? Here are a few things
1. It uses relevant input data.The goal is to get people to buy books, and the input to the model is what books people buy. You can't expect to get much more relevant than that.
2. It's transparent. You know exactly why the site is showing you these particular books, and if the system recommends a book you didn't expect, you have a pretty good idea why. That means you can make an informed decision about whether or not to trust the recommendation.
3. There's a clear measure of success and an embedded feedback mechanism. wants to sell books. The model succeeds if people click on the books they're shown, and, ultimately, if they buy more books, both of which are easy to measure. If clicks on or sales of related items go down, will know, and can investigate and adjust the model accordingly.
Weapons of Math Destruction reviews, in an accessible, non-technical way, what makes models effective -- or not. The emphasis, as you might guess from the title, is on models with problems. The book highlights many important ideas; here are just a few
1. Models are more than just math. Take a look at 's model above while there are calculations (simple ones) embedded, it's people who decide what data to use, how to use it, and how to measure success. Math is not a final arbiter, but a tool to express, in a scalable (i.e., computable) way, the values that people explicitly decide to emphasize. Cathy says that "models are opinions expressed in mathematics" (or computer code). She highlights that when we evaluate teachers based on students' test scores, or assess someone's insurability as a driver based on their credit record, we are expressing opinions that a successful teacher should boost test scores, or that responsible bill-payers are more likely to be responsible drivers.
2. Replacing what you really care about with what you can easily get your hands on can get you in trouble. In 's recommendation model, we want to predict book sales, and we can use book sales as inputs; that's a good thing. But what if you can't directly measure what you're interested in? In the early 1980's, the magazine US News wanted to report on college quality. Unable to measure quality directly, the magazine built a model based on proxies, primarily outward markers of success, like selectivity and alumni giving. Predictably, college administrators, eager to boost their ratings, focused on these markers rather than on education quality itself. For example, to boost selectivity, they encouraged more students, even unqualified ones, to apply. This is an example of gaming the model.
3. Historical data is stuck in the past. Typically, predictive models use past history to predict future behavior. This can be problematic when part of the intention of the model is to break with the past. To take a very simple example, imagine that Cathy is about to publish a sequel to Weapons of Math Destruction. If uses only purchase data, the Customers Who Bought This Also Bought list would completely miss the connection between the original and the sequel. This means that if we don't want the future to look just like the past, our models need to use more than just history as inputs. A chapter about predictive models in hiring is largely devoted to this idea. A company may think that its past, subjective hiring system overlooks qualified candidates, but if it replaces the HR department with a model that sifts through resumes based only on the records of past hires, it may just be codifying (pun intended) past practice. A related idea is that, in this case, rather than adding objectivity, the model becomes a shield that hides discrimination. This takes us back to Models are more than just math and also leads to the next point
4. Transparency matters! If a book you didn't expect shows up on The Customers Who Bought This Also Bought list, it's pretty easy for to check if it really belongs there. The model is pretty easy to understand and audit, which builds confidence and also decreases the likelihood that it gets used to obfuscate. An example of a very different story is the value added model for teachers, which evaluates teachers through their students' standardized test scores. Among its other drawbacks, this model is especially opaque in practice, both because of its complexity and because many implementations are built by outsiders. Models need to be openly assessed for effectiveness, and when teachers receive bad scores without knowing why, or when a single teacher's score fluctuates dramatically from year to year without explanation, it's hard to have any faith in the process.
5. Models don't just measure reality, but sometimes amplify it, or create their own. Put another way, models of human behavior create feedback loops, often becoming self-fulfilling prophecies. There are many examples of this in the book, especially focusing on how models can amplify economic inequality. To take one example, a company in the center of town might notice that workers with longer commutes tend to turn over more frequently, and adjust its hiring model to focus on job candidates who can afford to live in town. This makes it easier for wealthier candidates to find jobs than poorer ones, and perpetuates a cycle of inequality. There are many other examples predictive policing, prison sentences based on recidivism, e-scores for credit. Cathy talks about a trade-off between efficiency and fairness, and, as you can again guess from the title, argues for fairness as an explicit value in modeling.
Weapons of Math Destruction is not a math book, and it is not investigative journalism. It is short -- you can read it in an afternoon -- and it doesn't have time or space for either detailed data analysis (there are no formulas or graphs) or complete histories of the models she considers. Instead, Cathy sketches out the models quickly, perhaps with an individual anecdote or two thrown in, so she can get to the main point -- getting people, especially non-technical people, used to questioning models. As more and more aspects of our lives fall under the purview of automated data analysis, that's a hugely important undertaking. - Weapons of Math Destruction by Cathy O’Niel is an interesting and accessible read that provides some valuable insight into the world of big data, predictive analytics, and artificial intelligence. She identifies some very real technical and underlying scientific issues as the world races to advance these technologies. She persuasively points out that computer models can encode human bias into tools and that sometimes exacerbates these biases. Many of her examples are quite rich and compelling. Unfortunately, her desire to reinforce her own social beliefs infuse the same kinds of bias into her analysis that she asserts exist in current WMD’s, just in the opposite direction. One example is her discussion of Kronos’ psychological testing. While it is true that the model prevents some mentally ill applicants from gaining jobs that may be able to do, it also assists an honest company from avoiding a provably higher risk of costly employee issues, some of which could threaten the future viability of the company. This possible outcome should be acknowledged in a well crafted analysis.
As a research scientist in the field of analytics I support most all her research recommendations. The technology implementing analytic models (e.g. predictive analytics and AI) is often leaving the supporting science behind. Mastering the science behind, rather than just advancing, these analytic technologies is as important as it is currently unpopular for research investment. But, if these inquiries into “WMD†are to be defensible research with persuasive results they cannot be colored by social and logical biases like Ms. O’Neil’s personal definition of morality or what a utopian society would look like. - In this excellent book the author clearly explains in layperson's terms how commercial and government data models are affecting our lives and in many cases ruining some lives. For example, she describes a computer algorithm that decides the faith of prisoners up for parole. We think it will be less biased than human decision makers, but in fact the bias can be encoded in the algorithm, and because its details are hidden, and because it drives positive feedback loops, it can create very unfair outcomes (e.g. if it's racially biased against blacks, more and more black people get snared in its trap, seemingly validating the bias). Every technology has potential downsides and upsides, and big data models are no exception. The first step is to understand what's going on, and this book is a great place to start. She also gives examples of how these models can and are being used for good and also some potential ways the bad models can be brought under control. No math or statistical knowledge is required to understand the book.
- Excellent overview of how the misuse of statistics and big data can be harmful, but the book is short on prescriptions and I thought the criticism a bit overwrought in places. Nonetheless, the book is provocative, makes you think, and raises a number of challenging issues about how people can be unfairly harmed by statistical profiling (though the author doesn't fairly mention that the baseline before statistics was human judgement and bias, which can be and often is equally bad if not worse).
