Thursday, January 10, 2013

RTFB

Aunt Judy of the Attic has found a blog she likes, Mathbabe, aka Cathy O'Neill, and has quoted therefrom extensively, or at least one article someone sent to Atlanta

Distrust the experts.
Why? Because you don’t know their incentives, and they can make the models (including Bayesian models) say whatever is politically useful to them. This is a manipulation of the public’s trust of mathematics, but it is the norm rather than the exception. And modelers rarely if ever consider the feedback loop and the ramifications of their predatory models on our culture.
Well you can go over there and have a peek,  but Eli thought he would RTFB a bit, and it was interesting.  O'Neill is a data scientist, someone whose skill set involves structure and extracting useful information from the mass of glop called today's INTERNET or the data base of the week.

The blog was interesting, a bit off the area where Eli has much to say, but can learn a little by reading,
However, however,

There was another post with the Rabett Run class title,Data Science explained by the media, or: why I might just screw your wife while you are at work points to an article in Forbes (why this is so delicious the bunnies will see in a nonce) by Ray Rivera, Data Science:  Buyer beware, which is a slightly more skeptical brew and one O'Neill is unhappy, but a bit uncomfortable with

Rivera starts with an easy shot at everything else
Any field of study followed by the word “science”, so goes the old wheeze, is not really a science, including computer science, climate science, police science, and investment science.
so he obviously has little use for natural, physical or social science. but what the hell, data science for his is perilously close to management fad of the week and an outgrowth of the devil itself Business Process Reengineering (BPR), go read if you want details. but
Data science is the spry third generation of BPR, responding to vastly increasing IT capacity, unprecedented ability of businesses to create data, widespread realization that data is a valuable resource, and the burdensome need to extract data from storage in order to realize business value. Yet, data science belongs to a family tree of business practices that for over a century have been governed by technocrats who view organizations as machines, desiring to automate everything and eliminate people wherever possible. Data science is shaping up to be a redux of its grandfather BPR, with the same structural features (BPR was never really engineering, nor as we shall see is data science really science), and its propensity for sin and indulgence.
Patience, this is Rabett Run and we get there, bunnies enjoy the trip, but Eli does get there and hopefully everybunny learns a bit along the way.

Oh yes, here it is, Rivera points out that
As BPR morphed into knowledge management, the virtue of simplicity was reversed, and complexity came to indicate merit. Data science promises to deliver value by unpacking some of that complexity. Yet like the two generations of fads that preceded it, data science tries to create value through an economy of counterfeits:
  • False expertise, arising as persons recognized as experts are conversant in methods and tools, and not the underlying business phenomena, thereby relegating subject matter knowledge below methodological knowledge,
  • False elites, arising as persons are summarily promoted to high status (viz., “scientist”) without duly earning it or having prerequisite experiences or knowledge: functionaries become elevated to experts, and experts are regarded as gurus,
  • False roles, arising as gatekeepers and bureaucrats emerge in order to manage numerous newly created administrative processes associated with data science activities, yet whose contributions to core value, efficiency, or effectiveness are questionable,
  • False scarcity, arising as leaders and influencers define the data scientist role so narrowly as to consist of extremely rare, almost implausible combinations of skills, thereby assuring permanent scarcity and consequent overpricing of skills.
Great, let's look at the those
Seen that have we.  Demands for the laying on of statistical high priests in climate studies who don't have a clue about why carbon dioxide doesn't sink in the atmosphere, freaky econometricians, mining investors, and more.  Eli could go on and on and on, but you, dear readers can fill in the blanks
  • False elites, arising as persons are summarily promoted to high status (viz., “scientist”) without duly earning it or having prerequisite experiences or knowledge: functionaries become elevated to experts, and experts are regarded as gurus,
 IPCC Expert Reviewers:) (not all of them, of course, but certainly those who proudly proclaim the title)
  • False roles, arising as gatekeepers and bureaucrats emerge in order to manage numerous newly created administrative processes associated with data science activities, yet whose contributions to core value, efficiency, or effectiveness are questionable,
Auditors, FOIA demanders, rent seekers the ones, who in the words of the idiot tracker
uses the coercive power of the state to force other people to give him, gratis, the fruits of their labor. He does not produce himself — he uses the data of others, repackaged and sensationalized,
and finally
  • False scarcity, arising as leaders and influencers define the data scientist role so narrowly as to consist of extremely rare, almost implausible combinations of skills, thereby assuring permanent scarcity and consequent overpricing of skills.
Anybunny seen Honest Broker hereabouts?

10 comments:

Pinko Punko said...

Very good.

Anonymous said...

False Rules, arising as non-scientists define the (new) rules by which experts in the field must do their science ( mandating: sharing of data and computer code with every Tom, Dick and Tony regardless of whether they have the skills or knowledge to even do anything with it and regardless of whether any of it is proprietary and possibly covered by pre-existing agreements forbidding public sharing, "repetition" instead of "replication" of results, publication of all correspondence[emails] between scientists, allowing every crackpot and/or poorly written paper into scientific journals, public funding of crackpots who don't know the difference between a baseline and a clothes line, etc, etc )

~@:>

Miguelito said...

"Any field of study followed by the word “science”, so goes the old wheeze, is not really a science, including computer science, climate science, police science, and investment science"

Funny, but I thought climatology was climate science, which would fit very well into his definition of science.

susan said...

This is a classic case of turning meaning on its head. Either by reversing subject and object or asserting the exact opposite of the original, it is possible to do virtually no work to claim one knows better. The reverser originates neither the language nor the subject matter, but makes some simple substitions, and hey presto, the appearance of expertise.

It should no longer shock me that dishonest is so common. This technique is particularly annoying because it steals hard work and originality in the service of lies.

susan said...

substitutions, that was

aargh

Hank Roberts said...

"... since PR men disguising themselves as scientists have been around for decades. But I’d argue it’s a question that is increasingly urgent considering how much of our lives are becoming modeled.&

It would be great if substantive data scientists had a way of getting together to defend the subject against sensationalist celebrity-fueled noise.

One hope I nurture is that, with the opening of the various data science institutes such as
which was a announced a few months ago, there will be a way to form exactly such a committee. Can we get a little peer review here, people?

Hank Roberts said...

I blew the html above, that's all a quote from another of the Mathbabe blog pages.

Good stuff there.

Don't trust my links, you know how to find it.

Gavin Cawley said...

If the models (including Bayesian models) can be made to say whatever is politically useful to them, then the real question is "why haven't the skeptics made a GCM that can explain past climate without CO2 and the enhanced greenhouse effect?".

Gaz said...

"Any field of study followed by
the word “science”, so goes the
old wheeze, is not really a
science, including computer
science, climate science,
police science, and investment
science."

What about Blog Science? Surely we can make an exception! Such a promising field!

Anonymous said...

Eli is a naughty bunny for making me go into Judy's place*.

I always come away feeling dirty and sullied.

[Does the sign really say "Porky's Galore"?]