Big Dataz Isn’t Just Watching You—It’s **Designed** To Make You Poorer

[rayzer at riseup.net (Razer)]

There's are a number of reasons why 'teh gubmint wants ur dataz', and
it's not ONLY about killing 'terrorists' or suppressing potential
insurrection. It's about ripping you off for every penny they and their
corporate BFFs can shake out of you.

Rr

Big Data Isnâ??t Just Watching Youâ??Itâ??s Making You Poorer

Cathy Oâ??Neilâ??s new book, Weapons of Math Destruction, shows mathematical
models arenâ??t free of ideology.

BY Pankaj Mehta, In These Times, Sept 6 2016

By some estimates, humanity now produces 2.5 quintillion bytes of data
every dayâ??more than a hundred times the amount of data in the entire
Library of Congress. This data ranges from Facebook posts to
military-grade satellite photos. Increasingly, this data is analyzed by
complex mathematical models that determine more and more aspects of our
lives, from the advertisements we see to whether we have access to
private insurance. Yet despite their growing importance, these models
often remain hidden.

Advocates of such mathematical modeling, in both the public and private
sectors, portray it as a neutral and efficient alternative to fallible
and biased human decision-making. Mathematician, data scientist and
popular blogger Cathy Oâ??Neil, author of Weapons of Math Destruction: How
Big Data Increases Inequality and Threatens Democracy, doesnâ??t agree.
She argues that many mathematical models are ideological tools that
exacerbate oppression and inequality. Her examples range from the crime
models used by police departments to determine which neighborhoods to
patrol, to the recidivism models used by judges to hand out prison
sentences.

Oâ??Neil is passionate about exposing the harmful effects of Big
Dataâ??driven mathematical models (what she calls WMDs), and sheâ??s
uniquely qualified for the task. She earned a Ph.D. in math from Harvard
and landed a tenure-track at Barnard. But she became bored with the pace
and insularity of academia, and left to work as a quantitative analyst
at the hedge fund D.E. Shaw. There, she had a front-row seat for the
2008 financial crisis.

This experience fundamentally changed Oâ??Neilâ??s relationship to
mathematics. She realized that far from being a neutral object of study,
mathematics was not only â??deeply entangled in the worldâ??s problems but
also fueling many of them.â?? People in power were â??deliberately
[wielding] formulas to impress rather than clarify.â?? This
disillusionment led Oâ??Neil to get involved with Occupy Wall Street and
start educating the public about the dangers of WMDs through her blog,
MathBabe.

She is careful to point out that there is nothing inherently destructive
about mathematical modeling. Sophisticated data modeling enables much of
modern technology, from wireless communication to drug discovery. How
can one distinguish a destructive math model from an ordinary, or even
helpful, one? Oâ??Neil identifies three key features of WMDs: lack of
transparency, lack of fairness and, most importantly, operation on a
massive scale.

Oâ??Neil grounds her argument in case studies of WMDs in a variety of
settings: finance, higher education, the criminal justice system, online
advertising, employment decisions and scheduling, and credit and
insurance provision. The â??value-addedâ?? model for teacher evaluation,
which looks at improvements in individual studentsâ?? test scoresâ??a
favorite of the so-called â??educational reformâ?? movementâ??is touted as an
objective measure of a teacherâ??s worth. Yet this is far from the truth.
Oâ??Neil cites an analysis by blogger and educator Gary Rubinstein of New
Yorkâ??s 2010 value-added scores for public school teachers. Oâ??Neil
explains that â??Of teachers who taught the same subject in consecutive
years, one in four registered a 40-point difference. [This] suggests
that the evaluation data is practically random.â?? Oâ??Neil argues that this
is because the value-added model, which relies on predictions of student
performance, suffers from a built-in logical flaw: No statistical model
can accurately make predictions about a class of 25 or 30 studentsâ??the
sample size is too small. Yet the high-stakes testing regime continues
to wreak havoc on the trajectories of students and teachers alike.

Other WMDs, known as e-scores, use data such as ZIP codes, web-surfing
patterns and recent purchases to evaluate a personâ??s credit-worthiness.
Unlike the more familiar FICO credit scores that are freely available
and regulated by the government, these secretive e-scores are
â??unaccountable, unregulated and often unfair.â?? Whereas FICO scores are
based on your own financial history, e-scores compare you to other
people with similar profiles. This may seem benign, but it can result in
feedback loops that reinforce existing social inequities. If you live in
a poor ZIP code, then your e-score will drop, meaning less credit and
higher interest ratesâ??essentially, an algorithmic redlining of the poor
and working class.

More: http://inthesetimes.com/article/19364/the-numbers-do-lie