Weapons of Math Destruction How Big Data Increases Ineuality and Threatens Democracy Kindle ePUB / Pdf By Cathy ONeil – TXT, Kindle & PDF Read


review Weapons of Math Destruction How Big Data Increases Ineuality and Threatens Democracy

Weapons of Math Destruction How Big Data Increases Ineuality and Threatens Democracy

A former Wall Street uant sounds an alarm on mathematical modeling a pervasive new force in society that threatens to undermine democracy and widen ineuality   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 shocking book the opposite is true The models being used today are. Captivating Insightful And important A 50000 foot view of how automated big data is a great tool for understanding human nature How it has great promise to make our lives easy And yet a very real takedown of how systems engineers and corrupt power seekers like corporate executives and for profit universities misuse this powerful tool And the even worse cases where people start with good intentions like ridding school systems of bad teachers only to toss out false negativesI found Cathy O Neil s Weapons of Math Destruction a very important book that highlights a lot of what s been going on in America over the past 30 or 40 years For instance I just finished The New Jim Crow and wondered how the Supreme Court would continue to rule in favor of crime policing tactics that 1 target poor urban areas populated by mostly black men and 2 allow the police the ability to stop people willy nilly that they found suspicious despite the fact that minorities are caught in stop and frisk situations while most are innocently going about their lives Problem is many of these people going to jail for nuisance crimes possessing small amounts of marijuana open containers driving on expired tags etc Things that seem the exact definition of systematic racism But when O Neil lays out how systems engineers have written algorithms that send police to hot spots those rulings make sense For instance large crimes that we all want to stop car thefts burglaries rapes assault and murder are rare While petty and nuisance crimes jaywalking possessing weed noise violations vandalism etc common Based on the law of large numbers a program trying to optimize broken windows policing would send officers to a district with a higher concentration of people typically ghettos Which leads to petty arrests in those areas for crap that doesn t matter much Which sends even police into these areas making arrests for petty offenses and so on and so on In short these ghettos are caught in a negative feedback loop And the residents likely to end up in jail for something stupid Like smoking dope which based on most evidence is just as high among whites than blacks So a white 19 year old frat boy at Ohio State can smoke up at will with little possibility of being caught While a nearby ghetto dweller who works maintenance or in the office at the university or attends the university while living at home will have a greater likelihood of being arrested Just based on where they liveSame crime use of narcotics Two different outcomes That is what O Neill terms as a weapon of math destruction Since it is pervasive destructive and opaue And things get worse if the two hypothetical dope smokers get arrested Odds are the white frat boy s parents live in a safe suburban neighborhood and thus knows zero convicted felons So his court appointed recidivism score attained by another destructive math weapon will be lower than the ghetto dweller s who lives near many felons Thus the courts may lessen the frat boys charge to a misdemeanor while charging the ghetto dweller with a felony Due to that recidivism score Talk about kicking someone when they re downAnd when both released on parole and ordered to steer clear of felons the frat boy will have no problems following this While the ghetto kid thanks in part to the policing software noted above will be surrounded by them Which of course adds to the already disadvantaged the further risk of being pegged as a parole violator Smack O Neill lays out other ways that system engineers perpetuate injustice She takes down for profit universities like the University of Phoenix who actively target poor people with aspirations Not to help since a University of Phoenix degree costs tens of times than a community college degree while adding less salary Instead University of Phoenix and their ilk exist to cash in the student loans guaranteed by the government Yep Poor people who don t know any better are targeted by companies that give them less while charging them Using data driven web advertising another Weapon of Math Destruction all for a uick buck These are just a few WMD s that O Neill examines She looks at others credit scoring e scoring a sort of electronic credit score derived on you based on your social media friends and activities the USA Today college rankings which have lead to spiraling tuition costs while providing uestionable value All of these WMD s lead to increasing social stratification And in many ways drive the winner take all nonsense that gives big money to a handful of developers who program an appBut the nice thing is that O Neill ends by providing valuable insight into how when properly used deep dive statistics can actually help people For instance O Neill was part of a task force that examined New York City s homeless They uncovered the single uneuivocal variable that would keep people off the streets access to Section 8 housing And once these people were housed they d move on to get jobs since having a stable residence makes it easier to gain employment And thus less likely to end up on the streets And all this research came at a time when Mayor Bloomberg was contemplating reducing Section 8 housing So it s prove importantShe also points to other positive uses of algorithms all of which point to putting our compassionate human based morality ahead of the appearance of objective measurable efficiency with appearance being the operative word Since O Neill makes the cost of following these algorithms clear All in all Weapons of Math Destruction is the best science book I ve read over 2016 Since it focuses not only what we can do the science and how it makes things efficient but forces us to focus on the ethics the why we may choose a less efficient alternative as it may be just Especially when blindly accepting a model often degenerate to pseudoscience And that anti scientific narrative can be amplified if the person wielding the WMD is either greed or malicious 5 starsThat said YAY This is my 80th book of the year So I ve just completed my goal

review Ó E-book, or Kindle E-pub Ï Cathy ONeil

Opaue 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 race or neighborhood 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 from college to retirement O’Neil exposes the black box models that shape our future. This book did a nice job describing large scale data modeling and its pitfalls in a very accessible manner It is so easy to think of computer algorithms as unbiased however the author demonstrates how they really do discriminate Next time I teach a class involving statistics I may use this book to show students how it is dangerous to blindly believe the numbers Mon patron voulait que je tape les seins nus poor student can’t get a loan because a lending model deems him too risky by virtue of his race or neighborhood he’s then cut off from the kind of education that could His Christmas Cowgirl (Wildflower Ranch pull him out of The Doctors Dating Bargain poverty and a vicious spiral ensues Models are The Collection propping up the lucky and Whispers of Feathers punishing the downtrodden creating a “toxic cocktail for democracy” Welcome to the dark side of Big Data   Tracing the arc of a Mount série tome 3 - L'empire du mal person’s life from college to retirement O’Neil exposes the black box models that shape our future. This book did a nice job describing large scale data modeling and its Entrepreneurial Vernacular pitfalls in a very accessible manner It is so easy to think of computer algorithms as unbiased however the author demonstrates how they really do discriminate Next time I teach a class involving statistics I may use this book to show students how it is dangerous to blindly believe the numbers

Cathy ONeil Ï 9 free read

Both as individuals and as a society Models that score teachers and students sort resumes grant or deny loans evaluate workers target voters set parole and monitor our health all have pernicious feedback loops They don’t simply describe reality as proponents claim they change reality by expanding or limiting the opportunities people have O’Neil calls on modelers to take responsibility for how their algorithms are being used But in the end it’s up to us to become savvy about the models that govern our lives This important book empowers us to ask the tough uestions uncover the truth and demand chan. We like to think of mathematics as basically pure and free from the nastier side effects of human nature And this purity rubs off so that the closer a science is to being able to be described in numbers the highly we regard it so physic is seen as somehow higher than biology and economics than anthropology That is if you can predict behaviour on the basis of an algorithm whether that be the behaviour of a billiard ball or a homeless person then this is proper science and it has a claim to a kind of objective truth that sets it aside from being challengedAnd that is the point of this book it seeks to help shatter this illusion particularly in relation to the human sciences but also and perhaps importantly in relation to marketing insurance policing education and other social activities that are increasingly being modelled and even normalised by algorithms She refers to these algorithms as the weapons of math destruction in the title the title is better in English of course where we say maths rather than math but her point stands The destruction such algorithms can cause became clear to her while she was working in finance just before the 2008 crash It certainly isn t that she sees maths itself as being the problem she refers to herself almost immediately as a kind of math nerd and proud of that designation Her point is that these algorithms are dangerous because we tend to think of them as being purely objective and therefore the results they provide as being beyond uestion And this state is helped along by than just the fact we hold mathematics in such high regard It is furthered by the fact that often the algorithms that spit out these assessments of us are obscure unfair and grow exponentially These are the three conditions that the author believes makes an algorithm a likely WMD So in looking at these in turn is the algorithm is obscure and most of them tend to be as she says at one point they are the special sauce for many companies and so they need to remain hidden from the competition For instance if you have an algorithm that allows you to predict who is going to make an ideal partner for someone else then your dating site is going to make you lots of money You are hardly going to want to let your competition know you secret But the problem is that by keeping your algorithm obscure and hidden from outside analysis you can say nearly anything you like about its effectiveness if no one is then able to check You know this is a version of the they can vote any way they like as long as I get to count storyBut it isn t just outright fraud that is the problem here although that doesn t mean fraud isn t a problem Just as bad is the idea that often these models bury their false negatives That is if the algorithm says never employ anyone with green eyes they are under performers the company that follows this advice is unlikely to ever find out if this is true or not That s because they won t have employed anyone with green eyes to test the model so the model will be confirmed by default The problem is that many of the algorithms used say in policing can encourage over policing of certain populations this is set in the US so let s just call those certain black Hispanic and Muslim populations to save time and this over policing by defining certain populations in these ways are also likely to create the monster they were supposed to be eliminating A nice example is an algorithm that denies jobs to people according to their credit scores which then means these people are less able to pay off their debts which gives them a worse credit score which then confirms the risk vicious cycle anyoneOther examples focus on the use of psychometric tests for all manner of things but increasingly as a pre employment test You might think these ought to be relatively easy to game you know how stupid would you have to be to answer very true to the prompt I sometimes fly off the handle for no real reason You know unless you are going for a job in as a wrestler your employer is probably not going to be overly impressed with that answer But as she points out sometimes applicants for example for a job at McDonalds are asked to choose between one of two alternatives It is difficult to be cheerful when there are many problems to take care of or Sometimes I need a push to get started on my work I have no idea what the right answer to that choice is I m not even sure which of the two really applies to me although there have been times in my life when both of them have and in your life too I suspect That is it isn t at all clear what this is seeking to achieve but it is clear that there is likely to be a wrong answer in the sense that making that choice might leave you without a job But even this level of obscurity isn t her main worry at least you know you ve been asked a uestion here that might be used against you but it is too often easy to correlate factors to ensure that certain people are excluded due to their sex sexuality race social class and so on merely by the underlying assumptions of those programming these algorithms and the algorithms aren t objective examples of the purity of mathematics but rather socially produced ephemera that are likely to have been shaped by the social stereotypes of the society and the people who produce them The author mentions that curious fact that orchestras now appoint five times as many female players since auditions have been held with the player behind a curtain Who d have thought women would play so much better when they were hidden from sight Such shy and retiring little things bless them The solution is to ensure that algorithms used to judge us are open and available for anyone to check a condition that will become increasingly unlikely as these algorithms are proprietaryThe uestion of fairness isn t at all easy to address and for many of the reasons already mentioned One of the examples given is teacher scores being used to determine who should get pay increases and who should be removed from the profession Basically the idea is to compare student outcomes so that the teachers who do not increase student scores enough should be shown the door But as is pointed out here student scores aren t only affected by teacher performance And worse if you are teaching students who are either very far behind or very far in front you are unlikely to affect as big a movement in their scores as you might if you are teaching kids in the middle If you are going to be assessed on the basis of a score that score ought to be related to something you have control over rather than merely something that is relatively easy to measure The author points out that far too often the kinds of assessments made of teachers based on student attainment produce virtually random results year on year Since it is very unlikely that an exceptional teacher will become a very poor teacher from one year to the next then any assessment that produces such a result ought to be suspect And this is also true of loan application algorithms that judge you based upon the people you associate with For instance I ve read a few times lately that your credit rating can be impacted by your friends on FaceBook but even if this is not literally true algorithms are shown here to judge you by many factors that assume associations between you and other people or less likely to be a credit risk For instance if you buy things in certain stores as American Express admitted recently they restricted access to credit to people based on them freuenting particular stores The last condition of a WMD is that the algorithm can be scaled up to cover large populations An algorithm that works well in one set of circumstances might be both transparent and fair within its limited application but because it is then being used across a larger population it might suddenly stop being fair or reasonable This is because it creates a new norm in the population at large and that might well have seriously disadvantageous impacts on populations beyond the one it was originally intended to be used upon Again the examples I jump to tend to be associated with education where large scale testing programs have a disproportionate impact on poor communities which are defined as failing and then success becomes defined as doing well on the test so that the tail starts wagging the dog And this then has impacts on how kids get taught if you are only going to be measured by test results then we should just prime you for taking tests Which then makes education as boring as it is possible to make it for kids that were already struggling to see the point of education in the first place But the communities that do well on these tests are normally the already advantaged and they suffer none of these negative impacts because well they do well on these tests so no point priming them on of themLet them do artI really would recommend this book she gives lots and lots of examples and it is vitally important that we understand that this is the world we are increasingly moving towards More and of our lives are going to be influenced by algorithms and big data and yet too many of us are so terrified of mathematics that we will blame ourselves when these algorithms punish us It isn t at all clear to me how we might go about making these algorithms transparent fair or limited to a scale that keeps them safe but these are uestions we really ought to think about and act upon


10 thoughts on “Weapons of Math Destruction How Big Data Increases Ineuality and Threatens Democracy

  1. says:

    This was such a Malcolm Gladwell take on data science I think this book touches on an important subject and people should be aware of the issues O'Neil discusses But instead of doing a deep dive into the subject it just felt like a list of bad algorithms with instances of the people they hurt It didn't contain many examples of WMDs that I h

  2. says:

    O’Neil deserves some credit right off the bat for not waiting until her retirement from the hedge fund where

  3. says:

    Captivating Insightful And important A 50000 foot view of how automated big data is a great tool for understanding human na

  4. says:

    Welcome to the dark side of big data Thus the author concludes the Introduction section of this book Computers and the internet have e

  5. says:

    Is it legitimate to reduce people to the data that can be extracted from them?Please note that I put the original German text at the end of this review Just if you might be interestedEspecially the predictions mad

  6. says:

    This book did a nice job describing large scale data modeling and its pitfalls in a very accessible manner It is so easy to think of computer algorithms as unbiased; however the author demonstrates how they really do discr

  7. says:

    The subtitle of this book How Big Data Increases Ineuality and Threatens Democracy really says it all Big data has come into our lives in numerous ways and many of them are a scourge on our lives Big data in and o

  8. says:

    Big Data is opaue complicated managed by profit seeking corporations and is and dictating certain societal conditions from getting a job to applying to college to receiving healthcare Data on its own seems amoral a way to implement systems that are fair But O'Neil's point in this book is that all algorithms include basic assumptions and sometimes those basic assumptions are full of bias and not grounded in fact If the

  9. says:

    We like to think of mathematics as basically pure and free from the nastier side effects of human nature And this purity rubs off – so that the closer a science is to being able to be described in numbers the highly we regard it – so phys

  10. says:

    For the most part WMD is a rant with only impractical statements as solutions The author is onto something critically important when one reads the title of the book and goes through the first few pages However it is a tragedy to see the author falling in love with her own phrase WMD and completely losing the plot The examples used are good in the beginning but soon turn ridiculous they would be laughable if not so lamentable fo

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