Weapons of Math Destruction: How big data increases inequality and threatens democracy by Cathy O’Neil (2016)
This is a MUST READ book. Actually, buy it and actively share. Some of you may know about how computerized algorithms have caused chaos and massive profits in the financial sector — and crashes. Some of you may have been adversely impacted by computerized credit risk calculations. It is due to a math algorithm that airlines are able to juggle the price-fixing of their airplane tickets so you can never plan or know what the price for any given run or airline might be, making it tedious in the extreme for consumers to get the best price for any flight, other than you know that if you have to go somewhere last minute you will be screwed. The author is a genius at math in my opinion and a darn good writer. This book addresses some of the ways that we are being harmed by the use for math in ways that are destructive to us and commerce and our nation.
Many years ago, among other things, I did software training. People needed to know how to use the features of software properly in order to get correct results and to optimize their use of encoding for future revisions (such as not doing manual returns at the end of a line – really, back in the day this was a problem) and having a document format properly even on alternative printers and more.
Spreadsheets were the worst for user mistakes. First of all, in my experience, 9 times out of 10, a database should have been implemented instead of a spreadsheet designed by anyone in the business rather than an IT specialist. But more people knew spreadsheets (inadequately to be sure) and so that was what they used. Unfortunately, all too often the creators of the spreadsheets did not apply standard “double-entry bookkeeping” style formulas in the coding and if the expected amount was about what they expected, confident of their coding, they believed the results. I would venture to say that many people did not even bother to manually check the results. I remember reading about Canon Towels having based their accounting on flawed spreadsheets (or similar) and their spending turned out to exceed their assets and they had to declare bankruptcy. Alas I do not have the citation to my original reading about the spreadsheet issue and the Wikipedia article doesn’t mention that as a factor, but I was heavily involved with data integrity issues at the time and that is why I was attuned to such types of information.
I was going to write a paper on it. It boils down to the fact that organizations fail to properly train employees, expecting them to be “smart” enough to figure highly complex systems out on their own rather than pay someone on staff to conduct training and updates. Then they blame the employees when things go bad. Short-sighted fools all.
The number of times I found fundamental failures of basic understanding of how to use word processing, spreadsheets, or databases appalled me. But training was deemed too “costly” without measurable return. It is hard to prove especially while under unreasonable time constraints in today’s businesses. It might take 20 hours to find the flaw in a report because the person used an incorrect formula in one of dozens of cells, many of which are interdependent on others with no internal validation of the results. I have seen cases where it was easier to retype a word processed document from scratch rather than fix a badly done document. Furthermore, 90% of people (I’m guessing) do not know how to make various programs and data work together.
For example, how to download membership information from a database to use in word processing to make mailing labels. I actually witnessed a smart person typing the label information from scratch off a database printout when I happened to walk into the office for another reason. Since I was responsible for training, I was horrified and asked why they hadn’t called me to show them how to do the download. They didn’t call because it never occurred to them that an “export” was possible. I amended that notion immediately and made sure people called if they found themselves doing something stupid and tedious.
Without an in-house trainer, especially one that can rove around and have the authority to do random data checks for integrity, I do not believe that any company today really knows if the data they base decisions on is valid or not. Not being in the financial audit world, I cannot say if they actually look at the databases and spreadsheet formulas or code to look behind the curtain for errors or if they just check the data as a given in financial statements.
Another software issue came up when we were customizing and/or helping improve the design of a particularly specialized database. The developers had designed quite a few “reports” for our use, but had not included an “export” feature for us to be able to download the data and customize as might be needed for future reports. I pointed out that this was an essential component without which the software would be nearly worthless or very expensive to modify reports and export ability later. The company claimed it really wasn’t necessary and I remained adamant that it was critical. So they put it in. Within a week or two, it proved to be, as I predicted, essential to accomplishing a time-critical report and we could do what we needed to do. But someone who never heard of the concept of exporting data between application software would not have even recognized the absence of this capability.
Business and government and employees (for CYA) need to know what is behind the curtain.
Weapons of Math Destruction (WMD) brings what I considered to be a major flaw in business as usual into sharper focus. Whereas I did not suffer personally from Cannon bankruptcy, thousands of people did. In the cases the author presents, such errors, deliberate or accidental, actually do adversely impact me and everyone else so this is an absolutely CRUCIAL problem everyone has to recognize, even if you never have to touch a spreadsheet.
Misuse of algorithms corrupts absolutely, secretly, and without recourse is a danger and the author is quite right in presenting it as such!
Mathematicians and statisticians were studying our desires, movements, and spending power. They were predicting our trustworthiness and calculating our potential as students, workers, lovers, criminals.
This was the Big Data economy, and it promised spectacular gains. A computer program could speed through thousands of resumes or loan applications in a second or two and sort them into neat lists, with the most promising candidates on top. This not only saved time but also was marketed as fair and objective. After all, it didn’t involve prejudiced humans digging through reams of paper, just machines processing cold numbers. By 2010 or so, mathematics was asserting itself as never before in human affairs, and the public largely welcomed it. (pp. 2-3)
The case she discusses next is how these opaque algorithms go wrong, using the use of a Princeton developed teacher evaluation tool called IMPACT. The IMPACT score “represented half of her overall evaluation, and it outweighed the positive views from school administrators and the community. This left the district with no choice but to fire her, along with 205 other teachers who had IMPACT scores below the minimal threshold. (p. 4) This blows my mind. Getting decent teachers is almost impossible given the low-pay, lack of job security, unreasonable working conditions, and so many factors COMPLETELY BEYOND TEACHERS’ CONTROL.
Indeed, attempting to reduce human behavior, performance, and potential to algorithms is no easy job. (p, 5)
No kidding. Imperfect humans are the people writing the code that weights the elements and so incorporates assumptions and biases without factual basis.
But how much of that gap is due to her teacher? It’s hard to know, and Mathematica’s models have only a few numbers to compare. At Big Data companies like Google, by contrast, researches run constant tests and monitor thousands of variables. They can change the font on a single advertisement from blue to red, serve each version to ten million people, and keep tract of which one gets more clicks. They use this feedback to hone their algorithms and fine-tune their operation. While I have plenty of issues with Google, which we’ll get to, this type of testing is an effective use of statistics. . . .
What’s more, attempting to score a teacher’s effectiveness by analyzing the test results of only twenty-five or thirty students is statistically unsound, even laughable. . . . Statisticians count on large numbers to balance out exceptions and anomalies.
Equally important, statistical systems require feedback — something to tell them when they’re off track. Statisticians use errors to train their models and make them smarter. If Amazon.com, through a faulty correlation, started recommending lawn care books to teenage girls, the clicks would plummet, and the algorithm would be tweaked until it got it right. Without feedback, however, a statistical engine can continue spinning out faulty and damaging analysis while never learning from its mistakes. (pp. 6-7)
The IMPACT system has no feedback so the system is not able to know if it correctly identified bad teachers. They are fired and that’s that. No recourse to contest the almighty WMD.
Employers, for example, are increasingly using credit scores to evaluate potential hires. Those who pay their bills promptly, the thinking goes, are more likely to show up to work on time AND FOLLOW THE RULES. In fact, there are plenty of responsible people and good workers who suffer misfortune and see their credit scores fall. But the belief that bad credit correlates with bad job performance leaves those with low scores less likely to find work. Joblessness pushes them toward poverty, which further worsens their scores, making it even harder for them to land a job. It’s a downward spiral. And employers never learn how many good employees they’ve missed out on by focusing on credit scores. In WMDs, many poisonous assumption are camouflaged by math and go largely untested and unquestioned. (p.7)
No one even questions the validity of the criteria that establishes a credit score. For example, I discovered that my credit score had a “ding” because I had not bought a new car in ten years! Since I have been disabled for years, I do not have to drive very much, and so did not NEED to buy a new car! It would be stupid for me to get rid of a particular car to INCREASE MY CREDIT SCORE by taking on DEBT for the car loan PLUS the increased insurance costs and so on. Other “dings” happen if you CLOSE CREDIT CARDS. The assumption is that you must be in financial trouble to close accounts rather than you are being financially responsible by getting rid of credit cards you don’t need. It is a vested interest of credit reporters and credit card companies to have you keep cards because it is more likely you will use them.
You think your credit score will be high because you pay off your debt each month? Maybe not. Once again, the credit cards make money by you carrying a balance. I have had a line of credit reduced by thousands of dollars because I paid it off every month for a year. Subsequently, I ended up putting some vacation expenses on the card and just have let it ride, and voila! Twice now letters have arrived to congratulate me for achieving a higher credit line. Essentially this means that you have no idea what criteria are good for your score or if your boss weights it more heavily than letters of recommendation.
Many places require a certain credit score before they will rent an apartment to you. Well if you have just lost your job, and had your home foreclosed, and you need to rent a place to live, you might just be out of luck and there is NOTHING YOU CAN DO TO FIX IT. The numbers are incontestable.
This underscores another common feature of WMDs. They tend to punish the poor. This is, in part, because they are engineered to evaluate large numbers of people. They specialize in bulk, and they’re cheap. That’s part of their appeal. The wealthy, by contrast, often benefit from personal input. A white shoe law firm or an exclusive prep school will elan far more on recommendations and face-to-face interviews than will a fast-food chain or a cash-strapped urban school district. The privileged, we’ll see time and again, are processed more by people, the masses by machines. . . .
Verdicts from WMDs land like dictates from the algorithmic gods. The model itself is a black box, its contents a fiercely guarded corporate secret. This allows consultants like Mathematica to charge more, but it serves another purpose as well: if the people being evaluated are kept in the dark, the thinking goes, they’ll be less likely to attempt to game the system. Instead, they’ll have to WORK HARD, follow the rules, and PRAY that the model registers and appreciates their efforts. But if the details are hidden, it’s also harder to question the score or to protest against it. . . .
[Sarah Bax, a DC math teacher, asked] “How do you justify evaluating people by a measure for which you are unable to provide explanation?” But that’s the nature of WMDs. The analysis is outsourced to coders and statisticians. And as a rule, they let the machines do the talking. (pp. 8-9)
There are ways to cheat the system of course. So perhaps the previous year’s teacher had changed the test scores of the students lest they be fired or lose the bonus that would be theirs if the students ranking was higher than some other cohort. This disadvantages the next year’s class because the final previous year’s scores have been inflated. It is not clear why they were not testing at the start of the year, in fact it seemed that they were to determine how students performed from the start to the end of the year. So if students had received inflated grades, that should have been picked up by the initial testing of the new year.
One thing not questioned either was the validity of the tests the students were taking. Whose not to say that the students’ would not deliberately or casually fail testing? There are just too many factors to consider to make machine evaluation accurate. But that is what they do because ultimately, I think, the administrators are cowards, and lazy to boot. There competency does not seem to be questioned. I’m just guessing but somehow I suspect that the teachers are women and the majority of the administrators are men.
Do you see the paradox? An algorithm processes a slew of statistics and comes up with a probability that a certain person might be a bad hire, a risky borrower, a terrorist, or a miserable teacher. That probability is distilled into a score, which can turn someone’s life upside down. And yet when the person fights back, “suggestive” countervailing evidence simply won’t cut it. The case must be IRONCLAD. The human victims of WMDs, we’ll see time and again, are HELD TO A FAR HIGHER STANDARD of evidence than the algorithms themselves. (p.10)
She describes the MONEYBALL use of statistics (I loved that movie — it is one of few that I have watched multiple times, maybe should read the book too). With moneyball, the correlations are directly related to the functions of the ballgame. The data are available to everyone.
Moreover, their data is highly relevant to the outcomes they are trying to predict. This may sound obvious, but as we’ll see throughout this book, the folks building WMDs routinely lack data for the behaviors they’re most interested in. So they substitute stand-in data, or proxies. They draw statistical correlations between a person’s 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, don’t use proxies because they use pertinent inputs like balls, strikes, and hits. (pp. 17-18)
One very troubling discussion she provides presents the LSI-R questionnaire that inmates apparently MUST answer despite the Constitutional right to plead the 5th Amendment and not incriminate oneself. The form becomes a vicious cycle of perpetuating incarceration of the poor and disadvantaged with questions that are assumed to indicate the probability of recidivism. Some of the questions ask things like do your relatives or friends have criminal records. Another is if there was early “involvement” with police, which clearly is construed to make prisoners more likely to commit crimes again. However, given policies like “stop and frisk” and differential treatment in sentencing, a rich white kid gets off and doesn’t know anyone with criminal records but a poor kid is highly likely to have early involvement with police and gotten caught for minimal offenses by racist policing and judges who may use the LSI-R for sentencing. In other words, having criminal friends or family is actually going to be used to increase the sentencing rather than be based on the circumstances and actual crime being adjudicated.
This is unjust. The questionnaire includes circumstances of a criminal’s birth and upbringing, including his or her family, neighborhood, and friends. These details should not be relevant to a criminal case or to the sentencing. Indeed, if a prosecutor attempted to tar a defendant by mentioning his brother’s criminal record or the high crime rate in his neighborhood, a decent defense attorney would roar, “Objection, Your Honor!” And a serious judge would sustain it. This is the BASIS OF OUR LEGAL SYSTEM. We are judged by what we do, not by who we are. An although we don’t know the exact weights that are attached to these parts of the test, any weight above zero is unreasonable. (p. 26)
She calls this kind of circular reasoning on recidivism a “pernicious WMD feedback loop.”
But in fact the model itself contributes to a toxic cycle and helps to sustain it. That’s a signature quality of a WMD. (p. 27)
She cites the cost of prisons to US taxpayers as $70 billion a year. And we know that the prisoners are not benefiting from these absurd costs but they are, in fact, suffering at the hands of psycho guards. Quite a bit has come to light about women being denied decent sanitary products while in jail, even made to bleed all over their clothes because of arbitrary and punitive rules about how many pads or tampons they are allowed by arbitrary rules that do not take reality into consideration. Period lasts longer than 3 days? Too bad, you only get 3 days worth of supplies. Then they get punished for soiling their prison garb.
She worked as a “quant” for a hedge fund so she really understands how math was used to cheat. She quit in 2009 vowing to “fix the financial WMDs.”
The refusal to acknowledge risk run deep in finance. The culture of Wall Street is defined by its traders, and risk is something they actively seek to underestimate. This is a result of the way we define a trader’s prowess, namely by his “Sharpe ration,” which is calculated as the profits he generates divided by the risks in his portfolio. This ratio is crucial to a trader’s career, his bonus, his very sense of being. If you disembody those traders and consider them as a set of algorithms, those algorithms are relentlessly focused on optimizing the Sharpe ration. Ideally, it will climb, or at least never fall too low. So if one of the risk reports on credit default swaps bumped up the risk calculation on one of a trader’s key holding, his Sharpe ration would tumble. This could cost him hundreds of thousands of dollars when it came time to calculate his year-end bonus. (p. 46)
I don’t actually understand what she is describing here, but it sounds to me like taking risks is incentivized.
When she described a subsequent job, I was kind of appalled at the lack of ethical qualitiy that seemed apparent to me and reflected my own concerns about the criteria for pricing that companies like Expedia provide to users.
I stated out building models to anticipate the behavior of visitors to various travel websites. The key question was whether someone showing up at the Expedia site was just browsing or looking to spend money.
Those who weren’t planning to buy were worth very little in potential revenue. So we show them comparison ads for competing services such as Travelocity of Orbitz. If they clicked on the ad, it brought in a few pennies, which was better than nothing. However, we didn’t want to feed these ads to serious shoppers. In the worst case, we’d gain a dime of ad revenue while sending potential customers to rivals, where perhaps they’d spend thousands of dollars on hotel rooms in London or Tokyo. It would take thousands of ad views to make up for even a few hundred dollars in lost fees. (pp.46-47)
So she scoured data to assess what characteristics distinguish between serious shoppers from people just checking things out without intent to buy. Serious people did not get the ads from other travel sites. Just like the woman who first developed the algorithm to price airline seats to maximize profits, so you could pay $600 and the guy next to you may have paid $200, for reasons entirely beyond your control. To me, this is criminal, price gouging, and extortion. A series of random events means I happen to book a flight on a Wednesday and that is a slow day so I get a better price, or maybe it is a big day so I get scalped? Isn’t that what is illegal about concert tickets being bought up to inflate the prices? I don’t go to a store to buy a shirt and expect the price to be much different from one day to the next, apart from the freaking sales that pop up (though I think they made a law that said you have ten days and if the price was lowered, you get credit for the difference).
Medical care is the worst with the blind buying. They can’t tell you what something is going to cost until they process the “paperwork” and they can’t process the claim until you have the procedure. Classic Catch-22. Even then, you can be gobsmacked with unexpected bills after the fact (pain pills “self-administered” are not covered as in-network if you are on outpatient status IN a hospital, even if the pharmacy is IN NETWORK if you walk in from the street. Overhead is also added on a per pill basis, so a $1.00 pain pill can be billed to you for $20.25 and if you submit a claim, you will discover that insurance will only pay 80% of the $1.00 for the actual medicine and not a dime towards overhead. I hope to get my hands on some of the pricing calculations for hospital bills, like the ubiquitous plastic jug, bin, tissues (like sandpaper), and assorted other stuff you do not ask for but are billed regardless.
Likewise their is no limit whatsoever as to the maximum profit allowed by law; what the market will bear is what the saying declares is “reasonable” pricing. When applied to medicine, the entire fallacy of what the market will bear is revealed in all its glory as price fixing, price gouging, collusion, and corruption. Especially for prescriptions. Since a third party does most of the paying, there is no actual market per se. The prices of so many drugs are set beyond any sane and responsible or necessary minimum that the drug companies have to establish foundations to either supply the drugs at no cost or to even just cover the co-pays that can run to hundreds and possibly thousands of dollars. I know, I take one of each of these types of medication.
Witness the recent EpiPen scandal: a life-saving medication that was $50 or whatever (cheaper in other countries of course) was jacked up to $600.00 overnight and the CEO in charge got a pay raise to $17 MILLION dollars a year! Then after the public outcry they pretend to come up with a $300 “generic” themselves, but there is NO ACTUAL DIFFERENCE between the high priced one and the purportedly “generic” drug. Indeed the drug itself is pennies. It is the delivery system that is costly. Worst of all, the price did not increase because it had been improved, but simply because a new company bought the long-existing product and could jack up the price without actual consequences because however good a show the Congress put on about this behavior, I don’t think there is any law against it anymore, if there ever was. Just like the elimination of the usury cap on interest rates, leading to insane profits by credit cards and such like just because they CAN charge whatever they want, or like payday loans’ exploitation.
I wondered what the analogue to the credit crisis might be in Big Data. Instead of a bust, I saw a growing dystopia, with inequality rising. The algorithms would make sure that those deemed losers would remain that way. A lucky minority would gain ever more control over the data economy, raking in outrageous fortunes and convincing themselves all the while THEY DESERVED IT. (P. 48)
Chapter 3 makes another critical point about the application of algorithms, and that is that they don’t always scale. Anyone who has ever done any coding, knows that just because a system works on one or a few computers, it does not mean it will work with 200 people using it. Another one of my favorite issues that I find wrong with some programs is the failure to accommodate the exceptions. To devise a really robust system, you must be able to accommodate the inevitable exceptions. This applies to many aspects in life and it drives me nuts. Mainly, lately, it has been the failure of parking and transportation systems to accommodate disabilities. Our town is doing a wholesale renovation downtown and have not properly considered disability needs. But I am involved now and making that a little bit of a mission.
A formula, whether it’s a diet or a tax code, might be perfectly innocuous in theory. But if it grows to become a national or global standard, it creates its own distorted and dystopian economy. This is what happened in higher education. (p. 51)
She describes how colleges first became rated by US News & World Report, and what they based the ratings of the colleges on: “In the beginning, the staff . . . based its scores entirely on the results of opinion survey it sent to UNIVERSITY PRESIDENTS. There were a lot of complaints as you can well imagine.
This is how many models start out, with a series of hunches. The process is not scientific and has scant grounding in statistical analysis. In this case, it was just people wondering what matters most in education, then figuring out which of those variables they could count, and finally deciding how much weight to given each of them in the formula. (p. 53)
You can probably guess the rest of the story. A “second-tier” magazine became the definitive source for people choosing colleges. and “a vicious feedback loop materialized.” The inevitable happened, the colleges started working to the test so to speak.
It has been tying our education system into knots ever since, establishing a rigid to-do list for college administers and students alike. The U.S. News college ranking has great scale, inflicts widespread damage, and generates an almost endless spiral of destructive feedback loops. (p. 54)
However, when you create a model from proxies, it is far simpler for people to game it. This is because proxies are easier to manipulate than the complicated reality they represent. (p. 55)
She gives an example of someone using the number of Twitter followers as a criteria for a social media employee. Seems like a reasonable criteria.
But what happens when word leaks out, as it surely will, that assembling a crowd on Twitter is key for getting a job at this company? Candidates will soon do everything they can do to ratchet up their Twitter numbers. Some pay $19.95 for a service that populates their feed with thousands of followers, most of them generated by robots. As people game the system, the proxy loses its effectiveness. Cheaters wind up as false positives. (p. 55)
She goes on to discuss how this harms students and universities. Significantly one obvious criteria is not included: the price of tuition. There was a big scandal in China with massive sophisticated cheating. In China your whole life is decided by these exams. Really, seriously and profoundly may mean life and death. The students believed others were cheating so to have a fair chance, they had to cheat too.
In a system in which cheating is the norm, following the rules amounts to a handicap. (p. 63)
The whole process is now corrupted by the magazine’s WMD. New businesses began to coach and do the things that would score points on the admissions own WMD. Parents pay costs of $16,000 to the consultants to learn what their kids need to do and what the odds are for a kid to get into a particular school based on WMD criteria.
The victims, of course, are the vast majority of Americans, the poor and middle-class families who don’t have thousands of dollars to spend on courses and consultants. They miss out on precious insider knowledge. The result is an education system that favors the privileged. It tilts against needy students, locking out the great majority of them — and pushing them down a path toward poverty. It deepens the social divide.
But even those who claw their way into a top college lose out. If you think about it, the college admissions game, while lucrative for some, has virtually no educational value. The complex and fraught production simply re-sorts and reranks the very same pool of eighteen-year old kids in newfangled ways. They don’t master important skills by jumping through many more hoops or writing meticulously targeted college essays under the watchful eye of professional tutors. . . . All of them, from the rich to the working class, are simply being TRAINED TO FIT INTO AN ENORMOUS MACHINE — to satisfy a WMD. And at the end of the ordeal, many of them will be saddled with debt that will take decades to pay off. They’re pawns in an arms race, and it’s a particularly nasty one. (p. 65)
It is shocking to me now to learn this because I guess I got into college back before all this WMD stuff began. I did not have a helpful high school counselor who explained anything to me about how, for example, some universities require a minimum of a 3.8 grade average. I had it, so I didn’t worry about it, but I would not have been so cavalier about the certainty I could go to any college I wanted. I don’t think I even knew that there were for-profit colleges (“a money-sucking scourge”) that have been in the news a lot recently for basically being worthless, taking student loan monies and then not delivering an education that will get them a job in their field. Several of them have finally been closed down and of course there is the infamous failure, Trump University.
The problem is that all the systems to try to fix the problem depend on their own versions of WMDs, and these can be manipulated. Recently there has been a lot of discussion about how math and science courses were not really essential for some people and by making them take the courses as requirements to graduate, students can be harmed (low grade = scholarship loss) or give up pursing a particular career because of struggles with them or maybe foreign languages. It is well known that it is easy for children to learn foreign languages, almost effortlessly, but much harder for older students. So it is stupid that American schools wait until high school and make it a requirement for college graduation.
She continues to explain the numerous ways that any system can be gamed by multiple means, including eliminating the hard subjects as required. This does no one any favors. The dumbing down of the education to prop up your graduation numbers and GPAs is counterproductive.
She devotes an entire chapter to “the scourge of for-profit universities.” Another chapter (5) discusses “crime prediction software” — who knew! What could possibly go wrong with that?
But most crimes aren’t as serious as burglary and grand theft auto, and that is where serious problems emerge. When police set up the PredPol system, they have a choice. They can focus exclusively on so-called Part 1 crimes. These are the violent crimes, including homicide, arson, and assault, which are usually reported to them. But they can also broaden the focus by including Part 2 crimes, including vagrancy, aggressive panhandling, and selling and consuming small quantities of drugs. Many of these “nuisance” crimes would go unrecorded if a cop weren’t there to see them.
These nuisance crimes are endemic to many impoverished neighborhoods. In some places police call them antisocial behavior, or ASB. Unfortunately, including them in the model threatens to skew the analysis. ONCE NUISANCE DATA FLOWS into a PREDICTIVE model, more police are dawn into those neighborhoods, where they are more likely to arrest more people. After all, even if their objective is to stop burglaries, murders, and rape, they’re bound to have slow periods. It’s in the nature of patrolling. And if a patrolling cop see a couple of kids who look no older than sixteen guzzling from a bottle in a brown bag, he stops them. These types of low-level crimes populate their models with more and more dots, and the models send the cops back to the same neighborhood.
This creates a pernicious feedback loop. (pp. 86-87)
Also creates what I think of as a self-fulfilling prophecy. Officers expect to find more crime and sure enough they do. There may even be quotas for them as an “objective measure” of their job performance. I am not sure what the politicians mean when they say we need community policing, but to me it should me that instead of always LOOKING FOR TROUBLE the police should be visible, available, and accessible to help people. Instead of busting the 16-year olds for drinking, give them a warning and take the booze away. EVERYONE has done underage drinking. It should not ruin a bunch of kids’ lives. Similarly parking tickets, traffic stops for speeding (unless egregious 20 or 30 miles over the limit), accidental running of stop signs or red lights when there are no other vehicles or pedestrians. Just that the cops are laying in weight, possibly at a known location where mitigating factors make it hard to see the stop signs or the signage for parking is unclear.
In other words, please please please, stop picking the low fruit of nuisance crimes and catch the murders, rapists, and other violent offenders. Another phrase she uses is that of “victimless crimes” like kids drinking in public, or someone stopped and frisked and possessing a joint. No fines either. Fines are merely a method of taxing the poor because they often don’t have the money and there have been a lot of reports lately about people who get stuck in jail because they couldn’t pay fines and so late fees and interest and more gets piled on and on and they end up losing their jobs and so on. It is another vicious Catch-22. Only well-off people ever have to choose between buying food and paying a speeding ticket. The author points out that although supposedly “color-blind” and objective, the software is not because “In our largely segregated cities, geography is a highly effective proxy for race.”
If the purpose of the models is to prevent serious crimes, you might ask why nuisance crimes are tracked at all. The answer is that the link between antisocial behavior and crime has been an article of faith since 1982, when a criminologist named George Kelling teamed up with a public policy expert, James Q. Wilson, to write a seminal article in the Atlantic Monthly on so-called BROKEN-WINDOW policing. (p. 89)
This is a theory. And actual people were harmed in the implementation of the theory. She gives the example that Donald Trump recently cited as a “success” of “law and order” — the stop and frisk policy implemented in New York City that targeted non-whites and was therefore found discriminatory. NOTE: I thought it had been found UNCONSTITUTIONAL per the 4th Amendment, but in fact, horrifyingly, it was not — merely the implementation of it by targeting by race made it illegal. (Terry v. Ohio) I especially like the Wikipedia paragraph on the Douglas dissent:
Dissenting opinion of Justice Douglas
Justice Douglas strongly disagreed with permitting a stop and search absent probable cause:
- “We hold today that the police have greater authority to make a ‘seizure’ and conduct a ‘search’ than a judge has to authorize such action. We have said precisely the opposite over and over again.”
- “To give the police greater power than a magistrate is to take a long step down the totalitarian path. Perhaps such a step is desirable to cope with modern forms of lawlessness. But if it is taken, it should be the deliberate choice of the people through a constitutional amendment.”
Subsequently the Terry law has been expanded to an extreme degree so that even a very minor traffic offense allows searching without probable cause. One decision I was shocked to see was unanimous, and included Justice Ginsberg. Even more appalling, the court, in 2014, made it even broader to allow evidence gathered during a traffic stop even if the police officer had made a mistake in pulling a vehicle over in the first place, in this case, the officer thought a broken taillight was illegal but it was not a citation offense. It is clear from the commentary in Wikipedia that he targeted the driver as Hispanic and “nervous” and it is only his word that he ever saw the taillight out at all, since he claimed it came back on after he stopped the vehicle. It is not clear to me if the men in the car had a right to deny the police officer the search of the car or not. Since they actually had cocaine, they were stupid to say yes if they could have said no, but then wouldn’t that be another Catch-22 because denying a search would be suspicious and provide probable cause?
We are obsessed with “crime” that is always perceived as “street crime” but as she notes, there is no box on the PredPol forms for white collar crimes. That is, “the ones carried out by the rich.” (p. 90)
In the 2000s, the kings of finance threw themselves a lavish party. They lied, they bet billions against their own customers, they committed fraud and paid off rating agencies. Enormous crimes were committed there, and the result devastated the global economy for the best part of five years. Millions of people lost their homes, jobs, and health care.
We have every reason to believe that more such crimes are occurring in finance right now. If we’ve learned anything, it’s that the driving goal of the finance world is to make a huge profit, the bigger the better, and that anything resembling self-regulation is worthless. Thanks largely to the industry’s wealth and powerful lobbies, finance is underpoliced.
Just imagine if police enforced their zero-tolerance strategy in finance. They would arrest people for the slightest infraction, whether it was chiseling investors on 401ks, providing misleading guidance, or committing petty frauds. Perhaps SWAT teams would descend on Greenwich, Connecticut. They’d go undercover in taverns around Chicago’s Mercantile Exchange.
Not likely, of course. The cops don’t have the expertise for that kind of work [CRIME]. Everything about their jobs, from their training to their bullet-proof vests, is adapted to the mean streets. Clamping down on white-collar crime would require people with different tools and skills. The small and underfunded teams who handle that work, from the FBI investigators at the Securities and Exchange Commission, have learned through the decades that bankers are virtually invulnerable. . . . If the banks go south, our economy could go with them. (The poor have no such argument.) (p. 90)
She continues on page 91:
My point is that the police make choices about where they direct their attention. Today they focus almost exclusively on the poor. That’s their heritage, and their mission, as they understand it. And now data scientists are stitching this status quo of the social order into models, like PredPol, that hold ever-greater sway over our lives. . . .
The result is that we criminalize poverty, believing all the while that our tools are not only scientific but fair.
The vast number of innocent people led to stop and frisking capturing about 1 in 1,000 was “was linked in any way to a violent crime.” But obviously, if you stop 1,000 people there are going to be many that might have been underage drinking or other lesser crimes. She points out too, that many of the people targeted became angry and were then charged with the more serious crime of resisting arrest. Or as we all know from the choke-hold death of the developmentally disable man in NY, death.
If stopping six times as many people led to six times the number of arrests, the inconvenience and harassment suffered by thousands upon thousands of innocent people was justified. Weren’t they interested in stopping crime?
Aspects of stop and frisk were similar to WMDs, though. For example, it had a nasty feedback loop. It ensnared thousands of black and Latino men, many of them for committing the petty crimes and misdemeanors that GO ON IN COLLEGE FRATS, unpunished, every Saturday night. But while the great majority of university students were free to sleep off their excesses, the victims of stop and frisk were booked, and some of them dispatched to the hell that is Rikers Island. What’s more, each arrest created new data, further justifying the police. (pp. 93-94)
She has an amusing thought experiment about stop and frisk being done on the Gold Coast in Chicago, and the number of people who might refuse to get “out of his Mercedes and finding himself facing charges for resisting arrest.” Although she doesn’t note the latest trend of police killing black men for being black, the white man owner of a Mercedes is unlikely to fear anything such as deadly force or even rough handling when confronting the police in anger at being busted for something. They probably just would become aggrieved at having to spend money on lawyers instead of a nice vacation to Aspen for skiing.
But a crucial part of justice is equality. And that means, among many other things, experiencing criminal justice equally. People who favor policies like stop and frisk should experience it themselves. Justice cannot just be something that one part of society INFLICTS upon the other. (p. 96)
She then references back to the first chapter about the WMD that calculates risk of recidivism used for sentencing guidelines. “The biased data from uneven policing funnels right into this model. Judges then look to this supposedly scientific analysis, crystallized into a single risk score. And those who take this score seriously have reason to longer sentences to prisoners who appear to pose a higher risk of committing other crimes.” (p. 97) There is an extensive discussion of the flaws in the justice system on such a massive level and deliberately ignored. Among other things she cites evidence that the longer the prison term, the more likely the person can’t get a job when they get out and need to depend on food stamps and so on, which of course, are dramatically curtailed now as well. She doesn’t mention Universal Basic Income, and no doubt every Republican in the country and more would scream bloody murder at the thought of paying a basic income to felons, but WTF are they supposed to do? There is also the obvious impact of poverty and other issues that cause people to end up in jail and longer sentences for non-whites (like the rapist that got out in 3 months because he had a future). A black man with a joint gets 10 years (and marijuana should be legal anyway; for pity’s sake, cigarettes are lethal and cost millions or billions of taxpayer costs for medical care, but they are addictive and legal). She also cites other criteria that is not considered when assessing recidivism: solitary confinement in prison, rape, and though she didn’t cite it, we know that guards become thugs and commit hideous crimes against prisoners (like forcing women prisoners to give blow jobs for extra sanitary napkins). That might cause some acting out once freed.
OMG, she says that San Diego is using facial recognition software, so when they stop you, they take your photo and run it up to the cloud. The process was used on “20,600 people between 2011 and 2015.” Many were also forced (?) to provide DNA via mouth swabs. WTF? What about the right to not self-incriminate? I guess it got left behind when they developed DNA. Certainly I support the use of DNA, especially to identify rapists, but as we all know, the police are busy writing parking tickets for revenue rather than actually testing the rape kits so they might identify serial rapists, which are no doubt much more common that current records show.
Chapter 6 “Ineligible to Serve” freaked me out completely. Many retail chains make you take a personality test in order to get a job. Kronos/Unicru is one of them, and it asks very odd questions that have all sorts of implications in them, none of which are “can you use a cash register” or “do you show up on time” or other reasonable conditions. Essentially I would say that they are looking for docile, obedient, and pliable. Many of the questions sound like they are deliberately calculated to be lose-lose choices.
McDonald’s, for example, asked prospective workers to choose which of the following best described them:
“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.”
The Wall Street Journal asked an industrial psychologist, Tomas Chamorro-Preuzic, to analyze thorny questions like these. The first item. . . . captured “individual differences in neuroticism and conscientiousness”; the second, “low ambition and drive.” So the prospective worker is pleading guilty to being either high-strung or lazy.
A Kroger question was far simpler: Which adjective best describes you at work, unique or orderly?
Answering “unique”. . . captures “high self concept, openness, and NARCISSISM,” while “orderly” expresses conscientiousness and self control.”
Note that there’s no option to answer “all of the above.” [or none] Prospective workers must pick one option, without a clue as to how the program will interpret it. And some of the analysis will draw unflattering conclusions. . . . Certain patterns of answers, however, can disqualify them [applicants]. And WE DON’T KNOW WHAT THOSE PATTERNS ARE. We’re not told what the tests are looking for. The process is entirely opaque. (pp. 109-110)
There is no explanation of why you don’t get hired. And since so many companies are using the same tests, you can go on and on and be screwed every time. I know for a fact that I would never get a retail job again because (a) I would refuse to take the test, (b) I would never pass if I answered truthfully, (c) I could not even answer questions with such lame and unacceptable options, (d) I would not want to work for a company that sought to eliminate people based on personality — a very subjective and changeable quality that really doesn’t evaluate your ability to do a retail job. I may be insubordinate but maybe by questioning authority I will have a creative solution to a problem needing a solution. I probably would take violent action against a supervisor that sexually harassed me but that wouldn’t make me a bad employee.
Another discussion involved a medical school that tried to model for best prospective candidates. However, they based their data on historic data that incorporated the biases and prejudices of the time. Merely automating a process doesn’t eliminate bias.
One example I am very familiar with is in the arts, writing, painting, and musicianship. The author mentions that orchestras and such began doing blind auditions because no matter how many women tried out for them, they remained stubbornly rated lower than men. Men and women all judge women more harshly than men. In the case of musicians, race was also a factor due to the widespread belief that Asians are technically excellent but lack “emotion” or “passion” in their performances. Blind auditions helped, but I have followed this concept and elsewhere I read that someone had discovered that the auditions were still not blind enough to get a reasonable number of women hired. It turned out that people could hear the sound of their footsteps and could tell men’s shoes sounds to women’ts and that was enough to undermine the blind auditions. So they had the performers take off their shoes!
In the case of writing and painting, the exact same paper or painting was presented to a mixed group of people with the only difference being the names. Everyone consistently found some basis to judge the male name as better in some way, subtle or not, but clearly imaginary. When entering competitions now they frequently do them blind so that the names do not bias the judges. This also helps thwart racial discrimination for “black” sounding names or Asian or Latino. Ultimately though, people have to show up in person for a job and then the trouble begins, women who are assertive are bitchy, their job performances are judged more harshly, their pay is less than a man’s, they are deemed too emotional (because of periods of course), and on and on. Similarly, racial behaviors are judged against a generic white educated male and often found wanting for no justifiable reason.
Chapter 7 deals with on the job issues, like the limited discussions we have been hearing about concerning “clopening” where an employee has to bot closed the store and open it the next day. This leads to living difficulties with less sleep, less regular schedules, just-in-time on-call positions (without compensation for the fact that you cannot do anything much if you might be called into work on an hour’s notice), and the glory of all employment abuse, contract employees. Contract employees do not benefit from the limited and practically useless labor laws (underfunded, lack of enforcement) that would mandate overtime, or fixed schedules, or disallow hiring all part-timers at 39 hours a week to avoid paying overtime, benefits, and other labor laws being applied.
Starbucks was guilty of clopening schedules as well as lack of notice of planned schedules and pledged to reduce the problems.
A yer later, however, Starbucks was failing to meet these targets, or even to eliminate the clopenings, according to a follow-up report in the Times. The trouble was that minimal staffing was baked into the culture. In many companies, MANAGERS’ PAY is contingent on the EFFICIENCY of their staff as MEASURED BY REVENUE BY EMPLOYEE PER HOUR. [!!!!] . . . . What’s more, at Starbucks, if a manger exceeds his or her “labor budget,” a district manage is alerted, said one employee. And that could lead to a write-up. It’s usually easier just to change someone’s schedule, even if it means violating the corporate pledge to provide one week’s notice. (p. 126)
This is wrong on so many levels it is hard to rebut all the assumptions. Right off the bat, it is insane to base managers’ pay on something they have no control over: the number of customers and the dollars they spend. It is bad weather, so a slow night, and this is going to control wages? Unbelievable. Try to picture a bank manager whose salary depended on how much money was deposited in their branch in a day. They have no control, no way to predict, and the amount of money coming into the bank does not have anything to do with the job of managing the bank. Since they cannot predict the number of customers, they have to staff the tellers with at least two probably so they can cover for each other during breaks or float the day with one starting earlier and one staying later. Then say the manager has two tellers on a day with 10 customers who only withdrew money, it would be crazy to write up the manager for over-staffing because of a net loss of deposits. But of course, no Starbucks manager is going to complain because they would lose their job. That’s the way “at will” employment works. You can put up and shut up or work somewhere else.
The author considers scheduling software to be “one of the more appalling WMDs” because it is abusive to employees who are a “captive workforce.” The WMDs do not care that daycare closes at 6:00 p.m. or that you have to drive a child to school at 8:00 a.m. so it would be helpful to have a flex-schedule so you could come in at 9:00 instead of 8:00 in the morning. No, the WMDs objectives are not used to help employees; scheduling software only has the objective to make more profit for the company.
Following the New York Times report in 2014, Democrats in Congress promptly drew up bills to rein in scheduling software. But facing a Republican majority fiercely opposed to government regulations, the chances that their bill would become law were nil. The legislation died. (p. 130)
Chapter 8 is on credit scores, including the origin of FICO scores. Then there are things she calls e-scores that apparently are “stand-ins for credit scores” and are used for targeted marketing. Companies are apparently “legally prohibited from using credit scores for marketing purposes.” Although the credit card companies must have built in an exception to that rule, because there is a clear correlation between the number of credit card offers received and credit status. Often wrong of course, because such is the nature of WMDs.
She then describes how the use of credit scores have expanded beyond their original scope, as I think I mentioned previously, for job application criteria and being allowed to rent an apartment is a “good” section of town. Bad credit ratings cause reduced employment chances, causing more dings on the credit report, ad so on in a vicious circle.
Employers, naturally, have little sympathy for this argument. Good credit, they argue is an attribute of a responsible person, the kind they want to hire. But framing DEBT AS A MORAL ISSUE [for people, not corporations] is a mistake. Plenty of hardworking and trustworthy people lose jobs every day as companies fail, cut costs, or move jobs offshore. These numbers climb during recessions. And many of the newly unemployed find themselves without health insurance. At that point, all it takes is an accident or an illness for them to miss a payment on a loan. Even with the Affordable Care Act, which reduced the ranks of the uninsured, medical expenses remain the single biggest cause of bankruptcies in America.
People with savings, of course, can keep their credit intact during tough times. Those living from paycheck to paycheck are far more vulnerable. Consequently, a sterling credit rating is NOT just a proxy for responsibility and smart decisions [or rich parents]. It is also a PROXY FOR WEALTH. And wealth is highly correlated with race. (pp. 148-149)
We know now that the massive inequality of WEALTH in this country is the source of ongoing and dreadful consequences to non-white men. People of color and women get paid less and this is compounded by job stratification and percentage-based raises, as well as matching funds for retirement. Essentially condemns all but white men to a future of poverty and they control the tax codes and the Congress (itself full of millionaires) to make sure they get to keep all their money and pass it down to their children by lying about the infamous “death tax” that a bunch of ignorant hillbillies who support Trump think will ever have anything to do with them. Wage slaves remain slaves and are castigated for not saving more or investing in the casino of Wall Street (with what?!). They might be lucky enough to buy a house.
However, unlike the WEALTHY, they are not able to preserve this sole aspect of their wealth when they are older, because they will probably need long-term care. Unlike the rich with their lawyers and accountants who can bestow $5 million trust funds on their children, and avoid the death tax on their estates, and take other legal methods of preserving their wealth, people with only a house as their major asset cannot even leave it to their children. No, with the 5 year or is it now 7 year look back on asset disposition, you cannot retain your house until you need the highly overpriced and unregulated long term care, because you must instead sell it to pay for your care until the money is all gone, and even if you die before then, the money is nonrefundable and is attached to pay other medical bills and so forth. I think there is a song lyric, “thems that gots, gets” – oh, here is a link to the lyrics, it is a Billie Holiday song.
Consider this. As of 2015, white household held on average roughly ten times as much money and property as black and Hispanic households. And while only 15 percent of whites had ZERO or NEGATIVE net worth, more than a THIRD of blacks and Hispanic houehold found themselves with no cushion. This wealth gap increases with age. By their seixties, whites are eleven time richer than African Americans. Given these numbers, it is not hard to argue that the poverty trap created by employer credit checks affects society unequally and along racial lines. (p. 159)
And there is more. Like the purge of election rolls to eliminate all the Juan Gonzales names and the issue of credit report errors or identity theft, errors can have catastrophic effects if you are poor and you may not even know it. Wow, she cites some dreadful stories of people suffering under the pervasive and information “scraping” done by for profit companies that never get proofed and are sold to other companies as consumer profiles. An example she gives is of a woman who had been arrested for fights with her ex (I want to know if he got arrested too!) but she had never been convicted, and “had managed to have the records expunged from government databases. Yet the arrest records remained in files assembled by a company called RealPage, Inc., which provides background checks on tenants.” (p. 151)
Oh great, remember I mentioned above about getting your credit limit lowered because you paid off the balance every month, well I should have guessed it, but when you get a lowered credit rating, naturally it lowers your credit score. Argh. (p. 156)
Oh man: “In 2015, researchers at Consumer Reports conducted an extensive nationwide study looking for disparities in pricing. . . . What they found was wildly unfair, and rooted. . . in credit scores.”
Insurers draw these scores from credit reports, and then, using the insurer’s proprietary algorithm, create their own ratings, or e-scores. These are proxies for responsible driving. [!!!!] But Consumer Reports found that the e-scores, which include all sorts of demographic data, often count for MORE THAT THE DRIVER’S RECORD. In other words, how you mange money can matter more than how you drive a car. In New York State, for example, a dip in a driver’s credit rating from “excellent” to merely “good” could jack up the annual cost of insurance by $255. And in Florida, adults with clean driving records an poor credit scores paid an AVERAGE OF $1,552 more than the same drivers with excellent credit and a drunk driving conviction.
So why would models zero in on credit scores? Well, like other WMDs, automatic systems can plow through credit scores with great efficiency and at enormous scale. But I would argue the chief reason has to do with profits. If an insurer has a system that can pull in an extra $1,552 a year from a driver with a clean record, why change it? The victims of their WMD, as we’ve seen elsewhere, are more likely to be poor and less educated, a good number of them immigrants. They’re less likely to know that they’re being ripped off. And in neighborhoods with more payday loan offices than insurance brokers, it’s harder to shop for lower rates. In short, while an e-score might not correlate with safe driving, it dos create a lucrative pool of vulnerable drivers. Many of them are desperate to drive — their jobs depend on it. Overcharging them is good for the bottom line. (pp. 164-165)
This is followed by a discussion of how the marketeers are dividing people into “tribes” and assessing where they are going (hospital, mall) and base ads towards their assumptions the data tracking provides. This data will be fed into AI black boxes with no verification or even understanding of the code that presents assumptions about what you are going to do every day — even by the minute in the future. There is massive danger in this.
These automatic programs will increasingly determine how we are treated by the other machines, the ones that choose the ads we see, SET PRICES FOR US, line us up for a dermatologist appointment, or map our routes. They will be highly efficient, seemingly arbitrary, and utterly unaccountable. No one will understand their logic or be able to explain it. (p. 173)
Consumers will never know that their plane ticket costs are calculated doing a credit check of debt, mortgages, car registrations (Porsche=higher price? Nah, they get the lower price because they are more likely to travel more often because they clearly have discretionary spending money), and so much more. All unknowable to consumers and even less justifiable. No human being will be allowed to exercise human judgement or discretion to take other human factors into consideration that are not quantifiable. Then they will pretend that that makes if fair, objective, and indisputable.
In 1943, at the height of World War II, when American armies and industries needed every troop or worker they could find, the Internal REvenue Service tweaked the tax code, granting tax-free status to employer-based health insurance. This didn’t see to be a big deal, certainly nothing to rival the headlines about the German surrender in Stalingrad or Allied landings on Sicily. At the time, only about 9 percent of American workers received private health coverage as a job benefit. [and you can bet it was for management, not the secretaries] But with this new tax-free status, business set about attracting scarce workers by offering health insurance. Within ten years, 5 percent of Americans would come under their employer’s systems. Companies already exerted great control over our finances. But in that one decade, they gained a measure of control — whether they wanted it or not — over our bodies.
Seventy-five years later, health costs have metastasized and now consume $3 trillion per year. Nearly one dollar of every five we earn feeds the vast health care industry.
Employers, which have long been nickel and diming workers to lower their costs [aka raise their profits/CEO salaries], now have a new tactic to combat these growing costs. They call it “wellness.” It involves growing surveillance, including lots of data pouring in from the Internet of Things — the Fitbits, Apple Watches, and other sensors that relay updates on how our bodies are functioning. (p. 174)
Sounds thoughtful on the surface but it reminds me of the group we affectionately called the “bike Nazis” in one city I worked. They wanted to mandate that all city employees ride bikes to work. Naturally, since I lived on the side of a mountain, I expressed dismay and was told I could just wait for a bike-ready bus to take me up the mountain. Bike ready buses meant they had a rack for two bikes on the front. Never crossed their minds that a mom with a kid with a peanut allergy might need her car to get to the emergency room, or people often go to dentist and doctor appointments, or want to buy groceries more than what would fit in a pannier. The need to wear one set of clothes to get to work, then change into nicer clothes and shoes, with no shower after riding 5 miles on a bike was a minor issue to them. To them the benefits of fewer cars offset the extraordinary imposition on workers’ time (uncompensated) to get to and from work, and the host of other factors. Naturally, they did not even think about people with disabilities in this scenario, some of whom might not yet have “come out.”
Given a healthy dose of paranoia and what we already now know about WMDs, it will be no surprise to you that the “wellness” campaign to “help” people (that is, to reduce business premium costs for health insurance) so began to be a controlling and costly function that must surely be a violation of the ADA. She cites the case of a professor who had to comply with an Anthem “wellness” program by doing things that earn him “healthpoints” including assigning himself “monthly goals and getting more points if he achieves them. If he chooses NOT TO participate in the program, Abrams MUST PAY AN EXTRA $50 A MONTH toward his premium.” (p. 175)
This means, in effect, that in order to keep your job and your health insurance benefit, you must “follow a host of health dictates AND SHARE THE DATA NOT ONLY WITH HIS EMPLOYER but also with a third party company that administers the program.” This is wrong on so many levels. It also reminds me of a place I worked where they were “self-insured” which is to say that the employer directly knew exactly what medical expenses you were seeing doctors for because they paid the bills. They had a pool of funds allocated for average years. One year a man had a heart attack and really cost a large portion of the fund. The problem came when a woman employee subsequently had a problematic birth with a sick baby and they did not have enough money. So not only did the employer know all the details of your medical actions (including obviously mental health visits) but employees’ health and costs were a matter of debate. But as always, it could be worse. I am reminded of an often expressed fear (Galatacca like) that if you have gene testing and discover the breast cancer gene or heart disease probablity, this would become a “pre-existing condition” and allow insurance companies to deny coverage. Theoretically, Obamacare eliminated presexisting condition clauses, but given the Congress is Republican dominated and they hate 99% of the population, and have tried to overturn it 60 times or so, that option that was heavily used to eliminate actually covering sick people is fragile.
My fear goes a step further. Once companies amass troves of data on employees’ health, what will stop them from developing health scores and wiedling them to sift through job candidates? Much of the proxy data collected, whetr step counts or sleeping patterns, is NOT PROTECTED BY LAW, so it would theoretically be perfectly legal. And it would make sense. As we’be seen, they routinely refject applicants on the basis of credit scores and personality tests. Health scores represent a natural — and frightening — next step.
Already, companies are establishing ambitious health standards for workers and penalizing them if they come up short. Michelin, the tire company, sets its employees goals for metrics ranging from blood pressure to glucose, cholesterol, triglycerides, and WAIST SIZE [!!!!] Those who don’t meet the targets in three categories have to pay an extra $1,000 a year toward their health insurance. (p. 175)
“Alissa Fleck, a columnist for Bitch Media” called it for what it is: “humiliation and fat-shaming” and of course there is the fine. So the companies require you to spend your free time doing fitness points stuff, going to the doctor for blood work ($$$), and NOT COMPENSATE you for doing this and not recognizing that you have a LIFE and it sometimes means getting fast food for the family before going and sitting to watch a hockey game for a few hours.
The much used BMI is described as “a crude numerical proxy for fitness.” She notes that the BMI is “based on a formula devised two CENTURIES ago by a Belgian mathematician, Lambert Adolphe Jacques Quetelet, who knew next to nothing about health or the human body. He simply wanted an easy formula to gauge obesity in a large population. He based it on what he called the “average man.”
“That’s a useful concept,” writes Keith Devlin, the mathematician and science author. “But if you try to apply it to any ONE person, you come up with the absurdity of a person with 2.4 children. Averages measure entire populations and often don’t apply to individuals.” Devlin adds that the BMI, with numerical scores, gives “mathematical snake oil” the air of scientific authority.
The BMI is a person’s weight in kilograms divided by their height in centimeters. . . .It’s more likely to conclude that women are overweight. . . . What’s more, because fat weighs less than muscle, chiseled athletes often have sky-high BMIs. . . When ECONOMIC “STICKS AND CARROTS” are tied to BMI, LARGE GROUPS OF WORKERS are penalized for the kind of body they have. This comes down especially hard on black women, who often have high BMIs. (pp. 175-176)
I would add women who have had children too, since it is really hard to stay slim and maintain a pregnancy or lose weight after. This just exacerbates the “Twiggy” size 0 as ideal when in fact, women mostly have to starve to weigh so little, especially if, for example, you are taller than average as a woman.
But tying a flawed statistic like BMI to compensation, and compelling workers to mold their bodies to the corporation’s ideal, infringes on freedom. It gives the companies an excuse to PUNISH people they don’t like to look at — and to remove money from their pockets at the same time.
All of this is done in the name of health. Meanwhile, the $6 billion wellness INDUSTRY trumpets its successes loudly — and without offering evidence. (p. 177)
She notes that research proves that corporate wellness programs fail to improve overall health. Wellness programs do NOT reduce medical expenses, but the corporate revenue benefits from the fees and fines imposed on workers for not meeting spurious numerical goals.
Chapter 10 The Targeted Citizen is very shocking and I am very dismayed to learn about wholesale social experimentation by Facebook of literally altering feeds to include more sad feeds or more happy feeds to see if it alters behavior. (it does) Google, as everyone knows can control search results that would be able to completely demolish truth and promote falsehoods.
Two researchers, Robert Epstein and Ronald E. Robertson, recently asked undecided voters in both the United States and India to use a search engine to learn about upcoming elections. The engines they used were programmed to skew the search results, favoring one party over another. Those results, they said, shifted voting preferences by 20 percent. (p. 184)
She describes how a grocery shopping program could filter out people who might be “persuadable” to buy a more profitable brand of ketchup by sending them a coupon but not sending coupons to people who were already willingly paying full price. This was footnoted to say “Similarly, consumer websites are much more likely to offer discounts to people who are not already logged in. This is another reason to clear your cookies regularly.” (p. 189)
In late 2015, the Guardian reported that a political data firm, Cambridge Analytica, had paid academics in the United Kingdom to amass Facebook profiles of US voters, with demographic details and records of each user’s “likes.” They used this information to develop PSYCHOGRAPHIC analyses of more than FORTY MILLION voters, ranking each on the scale of the “big five” personality traits: openness, conscientiousness, extroversion, agreeableness, and neuroticism. Groups working with the Ted Cruz presidential campaign then used these studies to develop television commercials targeted for different types of voters, placing them in programming they’d be most likely to watch. (p. 191)
This process has been refined to show only a particular view of candidates to particular audiences. It is called microtargeting.
Successful microtargeting, in part, explains why in 2015 more than 43 percent of Republicans, according to a survey, still believed the lie that President Obama was a Muslim. And 20 percent of Americans believed he was born outside of the United States, and consequently an illegitimate president. (p. 194)
This shadow world of targeted advertising that some people never see is why you cannot understand why your neighbors may “passionately” believe what otherwise is known to you to be false.
It is not enough simply to visit the candidate’s web page, because they too, automatically profile and target each visitor, weighing everything from their zip codes to the links they click on the page, even the photos they appear to look at. It’s also fruitless to create dozens of “fake” profiles, because the systems associate each real voter with deep accumulated knowledge, including purchasing records, addresses, phone numbers, voting records, and even social security numbers and Facebook profiles. To convince the system it’s real, each fake would have to come with its own load of data. . . .
The result of these subterranean campaigns is a dangerous imbalance. The political marketers maintain deep dossiers on us, feed us a trickle of information, and measure how we respond to it. But we’re kept in the dark about what our neighbors are being fed. This resembles a common tactic used by business negotiators. They deal with different parties separately so that none of them knows what the other is hearing. This asymmetry of information prevents the various parties from joining forces — which is precisely the point of a democratic government. (p. 195)
As well all know too well now, even though California is, I think, the most populated state, the Presidential election is virtually over once Ohio and Florida votes come in because of the falseness of the Electoral College and the state variations of vote counting. For example, Florida going to W. could not have happened if they were not a winner take all state. In effect, none of the people who voted for Gore were represented by a single vote in the Electoral College. All states should be required to have open primaries, ranked voting, and exactly equal voting times, days, early voting, the same design of ballots, the same computer equipment — with hard copies produced for audits and third party objective independent review of the voting results. Absentee or early voting being hand-handled means that a person can see who received the votes and they can be all too easily discarded as we have witnessed in this 2016 primary.
As I write this, the entire voting population that matters lives in a handful of counties in Florida, Ohio, Nevada, and a few other swing states. Within those counties is a small number of voters whose opinions weigh in the balance. I might point out here that while many of the WMDs we’ve been looking at, from predatory ads to policing models, deliver most of their punishment to the struggling classes, political microtargeting harms votes of every economic class. . . .
In any case, the entire political system — the money, the attention, the fawning — turns to targeted votes like a flower following the sun. The rest of us are virtually ignored (except for fund-raising come-ons). The programs have already predicted our voting behavior, and any attempt to change it is not worth the investment.*
This creates a nefarious feedback loop. The disregarded voters are more likely to grow more disenchanted. The winners know how to play the game. They get the inside story, while the vast majority of consumers receive only market-tested scraps. (p. 196)
The * referred to a footnote:
At the federal level, this problem could be greatly alleviated by abolishing the Electoral College system. It’s the winner-take-all mathematics from state to state that delivers so much power to a relative handful of voters. It’s as if in politics, as in economics, we have a privileged 1 percent. And the money from the financial 1 percent underwrites the microtargeting to secure the votes of the political 1 percent. Without the Electoral College, by contrast, every vote would be worth exactly the same. That would be a step toward democracy. (p. 196)
The conclusion is an excellent, concise, reiteration of why WMDs are a serious threat to democracy and that is no exaggeration. I knew that are information supply was vulnerable, but I did not realize the extent of microtargeting and the feedback loops that serve to punish poor people and pretty much anyone not a multi-millionaire or billionaire. Be aware, be alert, and watch for WMDs that may be altering your life behind the shadows.