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Weapons of Math Destruction: How Big Data…
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Weapons of Math Destruction: How Big Data Increases Inequality and…

by Cathy O'Neil

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~ I will remember that I didn’t make the world, and it doesn’t satisfy my equations.
~ Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.
~ I will never sacrifice reality for elegance without explaining why I have done so.
~ Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.
~ I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension.
- From scientific oath by Emanuel Derman and Paul Wilmott

This is a very important albeit biased book, which should be viewed as a modern [b:The Limits to Growth|647942|The Limits to Growth|Donella H. Meadows|https://images.gr-assets.com/books/1479061377s/647942.jpg|634085]. There is the rave about big data and how it can improve our lives, but not a lot on how it can worsen lives of many people. The author tells the story of how big data and scoring algorisms, even created with benign intent to remove human biases and smooth processes, actually became weapons of math destruction or WND for short. Because while data are ‘objective’ this cannot be said about people who during modeling decide which data is important – after all, all models are simplifications.
Nowadays scoring, ranking and private algorithms affect our lives immensely – from a choice of university or job to loans and feed line on Facebook or search results on Google. A lot of this stuff is benign – after all private companies try to keep you on their pages/services. However, some aren’t.
Look for example on attempt of NY to improve quality of teachers in public schools by ‘weeding out’ bad teachers – ones with lowest ratings according to some metrics. The metrics is a secret to make it harder to evade of free-ride it. However, it is clear that pupils’ grades, and/or their change are an important part. Here is a story of Tim Clifford, a middle school English teacher in New York City, with twenty-six years of experience. his score by ‘value-added model’ was an abysmal 6 out of 100 in one year and 96 in the next without any change in his teaching style or ways, i.e. he was ranked as both worst and best teacher in just 2 consecutive years! And because the model’s algorithm is a secret, no one can say why. His assumption is that in the 1st year taught many special education students as well as many top performers. Needy students’ scores are hard to move because they have learning problems, and top students’ scores are hard to move because they have already scored high so there’s little room for improvement. The following year, he had a different mix of students, with more of them falling between the extremes. And the results made it look as though Clifford had progressed from being a failing teacher to being a spectacular one.
This is just a flavor. The book looks at many more cases in:
- Education
- Penitentiary
- Hiring
- Lending
- Voting
- Insuring
- Web-browsing
One important drawback is that the author sometimes exaggerates the problems or lessens the value of benefits.
Recommended to general world picture construction in our brave new world.
( )
  Oleksandr_Zholud | Jan 9, 2019 |
In the two years since Cathy O'Neil published this study of the danger posed by mathematical models—how their supposed neutrality merely reflects the biases of those who create them while creating closed-system feedback loops that warp society in unjust ways—it hasn't become any less relevant. In fact, not only does the book remain relevant, but subsequent revelations—most prominently those about Facebook's involvement with the 2016 US elections—show that O'Neil may even have been too cautious in what is a trenchant and prescient critique. ( )
  siriaeve | Dec 23, 2018 |
Be afraid... ( )
  LivingReflections | Dec 2, 2018 |
I was in a used book store looking for one thing and found something else (isn't that always the case?) I'd read Timandra Harkness's Big Data and am ever interested in the subject, so of course I bought this. Right in the introduction, Ms. O'Neil hit my confirmation bias sweet spot: "Big Data has plenty of evangelists, but I'm not one of them." If you are, maybe this will change your mind. Of course, that's silly...if you are an evangelist, you are probably profiting from the Weapons of Math Destruction she talks about here and are resolute.

College rankings, teacher scoring, neighborhood profiling for crime prevention, evaluating how hireable someone is, loan applications, insurability, evaluating convictions for sentencing, credit scores, targeted advertising, ...targeted election advertising...plus more...the message is the hammer to the forehead: Big Data and its exploiters are not your friend. Extensively researched, well composed, Ms. O'Neil's scary narrative should be a wake up, but is lost in, well, the big data.

On the U.S. News & World Report's college rankings, Ms. O'Neil observes that the first report might have seemed sensible, but "as the rankings grew into a national standard, a vicious feedback loop materialized." She continues: "The trouble was that the rankings were self-reinforcing." This correlates with an observation of mine back in 1998 on a report with a couple of lines tracking retention of women and minorities at the senior ranks in a Navy group: if you make the tracking a point because you perceive there is a problem, there will always be that problem.

In a Certified Public Manager course, our class was asked a "what if" scenario if budgets had to be cut significantly and the law enforcement participants said the first thing to go would be investigating robberies and small crime, as manpower would be limited and the more serious ones would take priority. The opportunists have tried establishing a foothold already by developing software that profiles neighborhoods to target for "nuisance crimes" - ostensibly to better use resources for the "bad" areas instead of the "good" ones. Pre-Minority Report (which she does mention as a lead in).

Before the inane No Child Left Behind directive, the inept [my word] Reagan administration released a flawed report based on erroneous data interpretation that blamed teachers for big drops in SAT scores. Thanks to the Bush mistake, teacher evaluations became part of the criteria of defunding and some of those evaluations were "based almost entirely on approximations that were so weak they were essential random." This is an example of a WMD that probably began with good intentions (like the college ranking) but the data weren't understood well and the model was broken before it left the shop.

This book was published in 2016, so was written before the horror show of the 2016 US election and the subsequent revelations of deliberate hacking, collusion, targeting by states and stateless groups, so Ms. O'Neil's analysis of targeted political advertisements is rather benign compared to the later all out efforts to sway and undermine the pseudo-democracy. She recalls Romney's gaffe at not realizing some people were present to his candid remarks about 47 percent of the population being takers who were not part of the demographic he was talking to. She says that "very likely cost [him] any chance he had of winning the White House." Fast forward four years to the facts-don't-matter right wing supporters and I'm guessing she's as dumbfounded that decency was thrown out the window, but not surprised at the skill of the Big Data manipulators in electing a meme (to borrow from a 4chan denizen quoted in another book.)

On a composition note, this book uses the end note format trend I detest: uncited in the text, listed by page number and the identifying sentence fragment, the excellent note sources are hidden until discovered. And because they are not cited in the text, it makes for a second pass through that annoys more than enhances - first time through it would be good to call attention to a source (a superscript does not detract from reading); seeing them at the end, I'm not inclined to go back and hunt for the passage the cite supports, no matter how intriguing that note is. "Gee, I wonder if that paragraph had a note. [Wastes time] No? Oh well, back to the next paragraph." Can you tell I think it is a bane and should be abandoned for traditional citation?

Regardless, this is a good book that needs to be read more widely. ( )
  Razinha | Nov 29, 2018 |
Dear Weapons,

Job changers can rationalize anything. That's ok. They probably need to. But you write about that in the first person a little too much for me to take it all as seriously as maybe I am supposed to?

This was a dnf for me. Not because it wasn't kinda interesting anyway, it was.

Not because even though I got tired of you explaining why you worked for the dark side til you didn't over and over again, because that was actually interesting too.

But because my ILL got recalled.

I'll look for you again because I still want to learn more about what you have to say.

Sincerely,
Don't want my privileges revoked ( )
  nkmunn | Nov 17, 2018 |
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Amazon.com Product Description (ISBN 0553418815, Hardcover)

A former Wall Street quant sounds an alarm on mathematical modeling—a pervasive new force in society that threatens to undermine democracy and widen inequality.
 
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 opaque, 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, 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 more responsibility for how their algorithms are being used. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.

(retrieved from Amazon Fri, 15 Apr 2016 20:07:53 -0400)

"A former Wall Street quantitative analyst sounds an alarm on mathematical modeling, a pervasive new force in society that threatens to undermine democracy and widen inequality,"--NoveList.

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