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◷ Free Format Kindle Read [ ✊ Weapons of Math Destruction : How Big Data Increases Inequality and Threatens Democracy ] ☭ Ebook Author Cathy O Neil ♬

◷ Free Format Kindle Read [ ✊ Weapons of Math Destruction : How Big Data Increases Inequality and Threatens Democracy ] ☭ Ebook Author Cathy O Neil ♬ ◷ Free Format Kindle Read [ ✊ Weapons of Math Destruction : How Big Data Increases Inequality and Threatens Democracy ] ☭ Ebook Author Cathy O Neil ♬ 1 BOMB PARTS What Is a Model It was a hot August afternoon in 1946 Lou Boudreau, the player manager of the Cleveland Indians, was having a miserable day In the first game of a doubleheader, Ted Williams had almost single handedly annihilated his team Williams, perhaps the games greatest hitter at the time, had smashed three home runs and driven home eight The Indians ended up losing 11 to 10 Boudreau had to take action So when Williams came up for the first time in the second game, players on the Indians side started moving around Boudreau, the shortstop, jogged over to where the second baseman would usually stand, and the second baseman backed into short right field The third baseman movedto his left, into the shortstops hole It was clear that Boudreau, perhaps out of desperation, was shifting the entire orientation of his defense in an attempt to turn Ted Williamss hits into outs In other words, he was thinking like a data scientist He had analyzed crude data, most of it observational Ted Williams usually hit the ball to right field Then he adjusted And it worked Fielders caught of Williamss blistering line drives than before though they could do nothing about the home runs sailing over their heads If you go to a major league baseball game today, youll see that defenses now treat nearly every player like Ted Williams While Boudreau merely observed where Williams usually hit the ball, managers now know precisely where every player has hit every ball over the last week, over the last month, throughout his career, against left handers, when he has two strikes, and so on Using this historical data, they analyze their current situation and calculate the positioning that is associated with the highest probability of success And that sometimes involves moving players far across the field Shifting defenses is only one piece of a much larger question What steps can baseball teams take to maximize the probability that theyll win In their hunt for answers, baseball statisticians have scrutinized every variable they can quantify and attached it to a value How much is a double worth than a single When, if ever, is it worth it to bunt a runner from first to second base The answers to all of these questions are blended and combined into mathematical models of their sport These are parallel universes of the baseball world, each a complex tapestry of probabilities They include every measurable relationship among every one of the sports components, from walks to home runs to the players themselves The purpose of the model is to run differentscenarios at every juncture, looking for the optimal combinations If the Yankees bring in a right handed pitcher to face Angels slugger Mike Trout, as compared to leaving in the current pitcher, how much likely are they to get him out And how will that affect their overall odds of winning Baseball is an ideal home for predictive mathematical modeling As Michael Lewis wrote in his 2003 bestseller, Moneyball, the sport has attracted data nerds throughout its history In decades past, fans would pore over the stats on the back of baseball cards, analyzing Carl Yastrzemskis home run patterns or comparing Roger Clemenss and Dwight Goodens strikeout totals But starting in the 1980s, serious statisticians started to investigate what these figures, along with an avalanche of new ones, really meant how they translated into wins, and how executives could maximize success with a minimum of dollars Moneyball is now shorthand for any statistical approach in domains long ruled by the gut But baseball represents a healthy case studyand it serves as a useful contrast to the toxic models, or WMDs, that are popping up in so many areas of our lives Baseball models are fair, in part, because theyre transparent Everyone has access to the stats and can understand or less how theyre interpreted Yes, one teams model might give value to home run hitters, while another might discount them a bit, because sluggers tend to strike out a lot But in either case, the numbers of home runs and strikeouts are there for everyone to see Baseball also has statistical rigor Its gurus have an immense data set at hand, almost all of it directly related to the performance of players in the game Moreover, their data is highly relevant to the outcomes they are trying to predict This may sound obvious, but as well see throughout this book, the folks building WMDs routinely lack data for the behaviors theyre most interested in So they substitute stand in data, or proxies They draw statisticalcorrelations between a persons zip code or language patterns and her potential to pay back a loan or handle a job These correlations are discriminatory, and some of them are illegal Baseball models, for the most part, dont use proxies because they use pertinent inputs like balls, strikes, and hits Most crucially, that data is constantly pouring in, with new statistics from an average of twelve or thirteen games arriving daily from April to October Statisticians can compare the results of these games to the predictions of their models, and they can see where they were wrong Maybe they predicted that a left handed reliever would give up lots of hits to right handed battersand yet he mowed them down If so, the stats team has to tweak their model and also carry out research on why they got it wrong Did the pitchers new screwball affect his statistics Does he pitch better at night Whatever they learn, they can feed back into the model, refining it Thats how trustworthy models operate They maintain a constant back and forth with whatever in the world theyre trying to understand or predict Conditions change, and so must the model Now, you may look at the baseball model, with its thousands of changing variables, and wonder how we could even be comparing it to the model used to evaluate teachers in Washington, D.C., schools In one of them, an entire sport is modeled in fastidious detail and updated continuously The other, while cloaked in mystery, appears to lean heavily on a handful of test results from one year to the next Is that really a model The answer is yes A model, after all, is nothing than an abstract representation of some process, be it a baseball game, an oil companys supply chain, a foreign governments actions, or a movie theaters attendance Whether its running in a computer program or in our head, the model takes what we know and uses it to predict responses in various situations All of us carry thousandsof models in our heads They tell us what to expect, and they guide our decisions Heres an informal model I use every day As a mother of three, I cook the meals at homemy husband, bless his heart, cannot remember to put salt in pasta water Each night when I begin to cook a family meal, I internally and intuitively model everyones appetite I know that one of my sons loves chicken but hates hamburgers , while another will eat only the pasta with extra grated parmesan cheese But I also have to take into account that peoples appetites vary from day to day, so a change can catch my model by surprise Theres some unavoidable uncertainty involved The input to my internal cooking model is the information I have about my family, the ingredients I have on hand or I know are available, and my own energy, time, and ambition The output is how and what I decide to cook I evaluate the success of a meal by how satisfied my family seems at the end of it, how much theyve eaten, and how healthy the food was Seeing how well it is received and how much of it is enjoyed allows me to update my model for the next time I cook The updates and adjustments make it what statisticians call a dynamic model Over the years Ive gotten pretty good at making meals for my family, Im proud to say But what if my husband and I go away for a week, and I want to explain my system to my mom so she can fill in for me Or what if my friend who has kids wants to know my methods Thats when Id start to formalize my model, making it much systematic and, in some sense, mathematical And if I were feeling ambitious, I might put it into a computer program Ideally, the program would include all of the available food options, their nutritional value and cost, and a complete database of my familys tastes each individuals preferences and aversions It would be hard, though, to sit down and summon all thatinformationoff the top of my head Ive got loads of memories of people grabbing seconds of asparagus or avoiding the string beans But theyre all mixed up and hard to formalize in a comprehensive list The better solution would be to train the model over time, entering data every day on what Id bought and cooked and noting the responses of each family member I would also include parameters, or constraints I might limit the fruits and vegetables to whats in season and dole out a certain amount of Pop Tarts, but only enough to forestall an open rebellion I also would add a number of rules This one likes meat, this one likes bread and pasta, this one drinks lots of milk and insists on spreading Nutella on everything in sight If I made this work a major priority, over many months I might come up with a very good model I would have turned the food management I keep in my head, my informal internal model, into a formal external one In creating my model, Id be extending my power and influence in the world Id be building an automated me that others can implement, even when Im not around There would always be mistakes, however, because models are, by their very nature, simplifications No model can include all of the real worlds complexity or the nuance of human communication Inevitably, some important information gets left out I might have neglected to inform my model that junk food rules are relaxed on birthdays, or that raw carrots are popular than the cooked variety To create a model, then, we make choices about whats important enough to include, simplifying the world into a toy version that can be easily understood and from which we can infer important facts and actions We expect it to handle only one job and accept that it will occasionally act like a clueless machine, one with enormous blind spots Sometimes these blind spots dont matter When we ask Google Maps for directions, it models the world as a series of roads, tunnels, and bridges It ignores the buildings, because they arent relevant to the task When avionics software guides an airplane, it models the wind, the speed of the plane, and the landing strip below, but not the streets, tunnels, buildings, and people A models blind spots reflect the judgments and priorities of its creators While the choices in Google Maps and avionics software appear cut and dried, others are far problematic The value added model in Washington, D.C., schools, to return to that example, evaluates teachers largely on the basis of students test scores, while ignoring how much the teachers engage the students, work on specific skills, deal with classroom management, or help students with personal and family problems Its overly simple, sacrificing accuracy and insight for efficiency Yet from the administrators perspective it provides an effective tool to ferret out hundreds of apparently underperforming teachers, even at the risk of misreading some of them Here we see that models, despite their reputation for impartiality, reflect goals and ideology When I removed the possibility of eating Pop Tarts at every meal, I was imposing my ideology on the meals model Its something we do without a second thought Our own values and desires influence our choices, from the data we choose to collect to the questions we ask Models are opinions embedded in mathematics Whether or not a model works is also a matter of opinion After all, a key component of every model, whether formal or informal, is its definition of success This is an important point that well return to as we explore the dark world of WMDs In each case, we must ask not only who designed the model but also what that person or company is trying to accomplish If the North Korean government built a model for my familys meals, for example, itmight be optimized to keep us above the threshold of starvation at the lowest cost, based on the food stock available Preferences would count for little or nothing By contrast, if my kids were creating the model, success might feature ice cream at every meal My own model attempts to blend a bit of the North Koreans resource management with the happiness of my kids, along with my own priorities of health, convenience, diversity of experience, and sustainability As a result, its much complex But it still reflects my own personal reality And a model built for today will work a bit worse tomorrow It will grow stale if its not constantly updated Prices change, as do peoples preferences A model built for a six year old wont work for a teenager This is true of internal models as well You can often see troubles when grandparents visit a grandchild they havent seen for a while On their previous visit, they gathered data on what the child knows, what makes her laugh, and what TV show she likes and unconsciously created a model for relating to this particular four year old Upon meeting her a year later, they can suffer a few awkward hours because their models are out of date Thomas the Tank Engine, it turns out, is no longer cool It takes some time to gather new data about the child and adjust their models This is not to say that good models cannot be primitive Some very effective ones hinge on a single variable The most common model for detecting fires in a home or office weighs only one strongly correlated variable, the presence of smoke Thats usually enough But modelers run into problemsor subject us to problemswhen they focus models as simple as a smoke alarm on their fellow humans Racism, at the individual level, can be seen as a predictive model whirring away in billions of human minds around the world It is built from faulty, incomplete, or generalized data Whether it comes from experience or hearsay, the data indicatesthat certain types of people have behaved badly That generates a binary prediction that all people of that race will behave that same way Needless to say, racists dont spend a lot of time hunting down reliable data to train their twisted models And once their model morphs into a belief, it becomes hardwired It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them Consequently, racism is the most slovenly of predictive models It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias In this way, oddly enough, racism operates like many of the WMDs Ill be describing in this book Ce texte fait r f rence l dition Broch.A New York Times Book Review Notable Book of 2016A Boston Globe Best Book of 2016One of Wired s Required Reading Picks of 2016One of Fortune s Favorite Books of 2016A Kirkus ReviewsBest Book of 2016A Chicago Public Library Best Book of 2016A Nature.com Best Book of 2016An On PointBest Book of 2016 New York Times Editor s ChoiceA Maclean s BestsellerWinner of the 2016 SLA NY PrivCo Spotlight AwardONeils book offers a frightening look at how algorithms are increasingly regulating people Her knowledge of the power and risks of mathematical models, coupled with a gift for analogy, makes her one of the most valuable observers of the continuing weaponization of big data She does a masterly job explaining the pervasiveness and risks of the algorithms that regulate our lives New York Times Book Review Weapons of Math Destruction is the Big Data story Silicon Valley proponents won t tell It pithily exposes flaws in how information is used to assess everything from creditworthiness to policing tactics a thought provoking read for anyone inclined to believe that data doesn t lie ReutersThis is a manual for the 21st century citizen, and it succeeds where other big data accounts have failed it is accessible, refreshingly critical and feels relevant and urgent Financial Times Insightful and disturbing New York Review of Books Weapons of Math Destruction is an urgent critique of the rampant misuse of math in nearly every aspect of our lives Boston GlobeA fascinating and deeply disturbing book Yuval Noah Harari, author of Sapiens The Guardians Best Books of 2016Illuminating ONeil makes a convincing case that this reliance on algorithms has gone too far The AtlanticA nuanced reminder that big data is only as good as the people wielding it WiredIf youve ever suspected there was something baleful about our deep trust in data, but lacked the mathematical skills to figure out exactly what it was, this is the book for you SalonONeil 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 recentlystarted an algorithmic auditing company She is one of the strongest voices speaking out for limiting the ways we allow algorithms to influence our livesWhile Weapons of Math Destructionis full of hard truths and grim statistics, it is also accessible and even entertaining ONeils writing is direct and easy to readI devoured it in an afternoon Scientific AmericanReadable and engaging succinct and cogent Weapons of Math Destruction is The Jungle of our age It should be required reading for all data scientists and for any organizational decision maker convinced that a mathematical model can replace human judgment Mark Van Hollebeke, Data and Society PointsIndispensable Despite the technical complexity of its subject, Weapons of Math Destruction lucidly guides readers through these complex modeling systems ONeils book is an excellent primer on the ethical and moral risks of Big Data and an algorithmically dependent world For those curious about how Big Data can help them and their businesses, or how it has been reshaping the world around them, Weapons of Math Destruction is an essential starting place National PostCathy ONeil has seen Big Data from the inside, and the picture isnt pretty Weapons of Math Destructionopens 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.Jordan Ellenberg, University of Wisconsin Madison, author of How Not To Be WrongONeil has become a whistle blower for the world of Big Data in her important new book Her work makes particularly disturbing points about how being on the wrong side of an algorithmic decision can snowball in incredibly destructive ways TIMEONeils work is so important her book is a vital crash course in the specialized kind of statistical knowledge we all need to interrogate the systems around us and demand better Boing BoingCathy ONeil, a number theorist turned data scientist, delivers a simple but important message Statistical models are everywhere, and they exert increasing power over many aspects of our daily lives Weapons of Math Destruction provides a handy map to a few of the many areas of our lives over which invisible algorithms have gained some control As the empire of big data continues to expand, Cathy ONeils reminder of the need for vigilance is welcome and necessary American ProspectAn avowed math nerd, ONeil has written an engaging description of the effect of crunched data on our lives Hicklebees, San Francisco Chronicle By tracking how algorithms shape people s lives at every stage, O Neil makes a compelling case that our bot overlords are using data to discriminate unfairly and foreclose democratic choices If you work with data, or just produce reams of it online, this is a must read ArsTechnica Lucid, alarming, and valuable ONeils writing is crisp and precise as she aims her arguments to a lay audience This makes for a remarkably page turning read for a book about algorithms Weapons of Math Destruction should be required reading for anybody whose life will be affected by Big Data, which is to say required reading for everyone Its a wake up call a journalistic heir to The Jungle and Silent Spring Like those books, it should change the course of American society Aspen Times O Neil s propulsive study reveals many models that are currently micromanaging the US economy as opaque and riddled with bias NatureYou dont need to be a nerd to appreciate the significance of ONeils message Weapons is a must read for anyone who is working to combat economic and racial discrimination Goop Cathy ONeils book is important and covers issues everyone should care about Bonus points its accessible, compelling, and something I wasnt expecting really fun to read Inside Higher EdOften we dont even know where to look for those important algorithms, because by definition the most dangerous ones are also the most secretive Thats why the catalogue of case studies in ONeils book are so important shes telling us where to look The GuardianONeil is passionate about exposing the harmful effects of Big Datadriven mathematical models what she calls WMDs , and shes uniquely qualified for the task She makes a convincing case that many mathematical models today are engineered to benefit the powerful at the expense of the powerless and has written an entertaining and timely book that gives readers the tools to cut through the ideological fog obscuring the dangers of the Big Data revolution In These TimesIn this simultaneously illuminating and disturbing account, ONeil describes the many ways in which widely used mathematic modelsbased on prejudice, misunderstanding, and biastend to punish the poor and reward the rich She convincingly argues for both responsible modeling and federal regulation An unusually lucid and readable look at the daunting algorithms that govern so many aspects of our lives Kirkus Reviews starred Even as a professional mathematician, I had no idea how insidious Big Data could be until I read Weapons of Math Destruction Though terrifying, its a surprisingly fun read ONeils vision of a world run by algorithms is laced with dark humor and exasperationlike a modern day Dr Strangelove or Catch 22 It is eye opening, disturbing, and deeply important.Steven Strogatz, Cornell University, author of The Joy of xThis taut and accessible volume, the stuff of technophobes nightmares, explores the myriad ways in which largescale data modeling has made the world a less just and equal place ONeil speaks from a place of authority on the subject Unlike some other recent books on data collection, hers is not hysterical she offers of a chilly wake up call as she walks readers through the ways the big data industry has facilitated social ills such as skyrocketing college tuitions, policing based on racial profiling, and high unemployment rates in vulnerable communities eerily prescient Publishers Weekly Well written, entertaining and very valuable Times Higher Education Not math heavy, but written in an exceedingly accessible, almost literary style O Neil s fascinating case studies of WMDs fit neatly into the genre of dystopian literature There s a little Philip K Dick, a little Orwell, a little Kafka in her portrait of powerful bureaucracies ceding control of the most intimate decisions of our lives to hyper empowered computer models riddled with all of our unresolved, atavistic human biases Paris ReviewThrough harrowing real world examples and lively story telling, Weapons of Math Destruction shines invaluable light on the invisible algorithms and complex mathematical models used by government and big business to undermine equality and increase private power Combating secrecy with clarity and confusion with understanding, this book can help us change course before its too late.Astra Taylor, author of The Peoples Platform Taking Back Power and Culture in the Digital Age Weapons of Math Destructionis 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.Cory Doctorow, author of Little Brother and co editor of Boing BoingMany algorithms are slaves to the inequalities of power and prejudice If you dont want these algorithms to become your masters, read Weapons of Math Destruction by Cathy ONeil to deconstruct the latest growing tyranny of an arrogant establishment.Ralph Nader, author of Unsafe at Any SpeedIn 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.danah boyd, founder of Data Society and author of Its Complicated 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 its an important one We need to reckon with technology Linda Tirado, author of Hand to Mouth Living in Bootstrap AmericaNext time you hear someone gushing uncritically about the wonders of Big Data, show them Weapons of Math Destruction Itll be salutary.Felix Salmon, Fusion Ce texte fait r f rence l dition Broch. Weapons of Math Destruction How Big Data Increases Buy Weapons Inequality and Threatens Democracy on FREE SHIPPING qualified orders Kindle edition by Cathy O Neil Download it once read your device, PC, phones or tablets Use features like bookmarks, note taking highlighting while reading Instruction Upbeat Downstairs Public Bulletin At New York s Kennedy airport today, a person later discovered to be public school teacher, was arrested trying board flight in possession ruler, protractor, drafting triangle, compass, calculator ABOUT THE BOOK We live the age algorithm Increasingly, decisions that affect our lives where we go school, whether get car loan, how much pay for health insurance are being made not humans, but mathematical models Total US Firearms Not Million, Million The numbers all over place, many them seem recursively refer one another, exactly building confidence rigor their development But they cluster around Narrative friendly million what if number is wrong believe correct Math Quest List roomrecess A list skills xkcd Lisp Cycles This work licensed under Creative Commons Attribution NonCommercial License means you re free copy share these Mass shooting statistics United States Sep , places change, choice weapon remains same In States, people who want kill lot other most often do with guns It an important popular fact things always as For instance, planet earth, man has assumed he intelligent than dolphins because had achieved so wheel, York, wars fab Mathonwy Fourth Branch Mabinogi son lord Gwynedd, Pryderi twenty cantrefs South Those were seven Dyfed, Morganog, four Ceredigion three Ystrad Tywi time, could except when his feet enfolded lap maiden, unlessmathbabe Exploring venting about quantitative issues issues guest post Toby Napoletano, philosophy PhD working politics podcast subject this I ve taught quite few introductory ethics courses undergraduates past five years data scientist author blog mathbabe She earned mathematics from Harvard at Barnard College before moving private sector, she worked hedge fund D E Shaw Cathy era blind faith big must end TED Talk Subtitles Transcript Algorithms decide gets job interview, don t automatically make fair Mathematician coined term algorithms secret, harmful weapons math destruction Learn hidden agendas behind formulas National Book Foundation Who did write book wrote average alternates between mystified intimidated growing power livesI wanted demystify empower first goal book, then, survey modern landscape, expose powerful ORCAA Ethical Stakeholder Matrix construction world bio ethics, ethical stakeholder matrix way determining answer question, does What washing There widely held belief involved, neutral widespread misconception allows bias unchecked, companies organizations avoid responsibility hiding myCares CARES can difference helps communities measure performance identify improve cardiac arrest survival rates By joining CARES, gain just access information will help save April Creampie Porn Videos Sex Movies Redtube Tons April porn videos XXX movies waiting Redtube Find best right here discover why sex tube visited millions lovers daily Nothing highest quality Titus Wikipedia Thaddeus Michael Bullard Sr born American professional wrestler former football player He currently signed WWE, performs Raw brand ring name Titus played college University Florida, thereafter Arena Football League AFL His career began Rush Cowan head women basketball coach Immaculata led consecutive AIAW national titles Mighty Macs six final appearances her seasons attaining record inducted into Basketball Hall Fame Brittany Neill Mrs hardcore pics Brittany classy lady turned hot pornslut pornMILF, HAD fuck EPIC EPIC Advisory Board Anita Allen, Henry R Silverman Professor Law Philosophy, Pennsylvania School L Allen expert privacy law, privacy, bioethics, contemporary values, recognized scholarship legal philosophy, rights, Trinity Missions Missionary Servants Most Holy Our Catholic Congregation priests Brothers different missions located Puerto Rico, Colombia, Costa Rica, Honduras, Mexico FolkLib Index Wisconsin Session Players M,N,O Links Musicians whose last names start letters M,N,O Band Members no known solo albums Pudsey Pacers Home Melissa Stead also featured miscellaneous team completing leg Weapons of Math Destruction : How Big Data Increases Inequality and Threatens Democracy

 

    • Weapons of Math Destruction : How Big Data Increases Inequality and Threatens Democracy
    • 1.2
    • 37
    • Format Kindle
    • 272 pages
    • 0141985410
    • Cathy O Neil
    • Anglais
    • 03 July 2016

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