Read Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy By Cathy O'Neil
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Ebook About NEW YORK TIMES BESTSELLER • A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric—with a new afterword “A manual for the twenty-first-century citizen . . . relevant and urgent.”—Financial Times NATIONAL BOOK AWARD LONGLIST • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review • The Boston Globe • Wired • Fortune • Kirkus Reviews • The Guardian • Nature • On Point We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules. But as mathematician and data scientist Cathy O’Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination—propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data.Book Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Review :
This book is an extended essay where the author is trying to make a point about how algorithms can be damaging to our communities.Unfortunately the logic in the book is a dumpster fire. I was astonished given that the author holds a PhD in mathematics... a very logical discipline.The main thesis of the book is that there are certain conditions for an algorithm in which it can become a 'weapon of math destruction', and tries to show examples of these cases. O'Neil is decidedly anti-big data and anti-modeling in this book.Here are my main complaints:1. Her treatment of all of the examples is offensive to the experts who actually do social science in those fields. She clearly has only a surface knowledge of these issues, makes many factual errors, and does not actually know what current social scientists are working on. For example, in the section about policing, O'Neil says that if the Chicago Police Department hired her as their data scientist (!) she could make these biases and issues with the models go away, all while completely oblivious to what current economists, sociologists, and other experts are working on.2. The claims made by O'Neil in this book are all testable hypotheses, however she makes NO effort to use data to make her argument, and instead relies on scant anecdotes and sweeping generalizations.3. O'Neil was contradictory as to whether people are the problem or algorithms are the problem. For example, in the section about Starbucks and employee scheduling software she slammed the managers who took control over the algorithm, but then later explained that we don't have enough people actually being involved who adjust the algorithms as necessary... So which is it?4. She misses the nuance between 'good' and 'bad' aspects of models. For example, when discussing the US News rating system for colleges, she argues that it isn't appropriate to rank schools. Then she goes on to attack for-profit colleges, while failing to acknowledge that the US News rating system can help guide someone who is underprivileged and doesn't have college counselors to tell them that the for-profit colleges are terribly terribly ranked.5. She needs to look up the word 'arbitrary' in the dictionary. I'll quote the definition here: "based on random choice or personal whim, rather than any reason or system". Many times throughout the book she describes the choices of models in her examples as 'arbitrary'. A model is the exact OPPOSITE of arbitrary. It makes choices based on the defined rules of the program...6. There is no original content or analysis in this book, beyond her coining of the phrase of 'weapons of math destruction'.7. I'm confused why people say the book is well written. It isn't. It rambles and often strays away from the thesis.In short, she does a disservice to the nuance involved with data and algorithms. She identifies some of the important issues near the beginning (e.g. sample size, out-of-sample conclusions, poor objective functions), however, her poor understanding of her examples, and hack-job of an argument is unfortunate and ultimately damning. 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. 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