How “Big Data” Went Bust | Slate

How “Big Data” Went Bust; ; In Slate; 2017-10-16.
Teaser: And what comes next.

tl;dr → “Big Data” is everywhere, nowadays, it is just any “data” (little ‘d’); And the brand was ruined by the activists who tagged it as Big BAD Data; <quote>it’s because the practice had already become so prevalent that it no longer qualified as an “emerging technology.”</quote>
and → Big Data is Facebook; Facebook is bad.
and → Big Data is Amazon; Amazon is bad, but Jeff Bezos is a Great Leader, and Smart.
and → concludes as <quote>perhaps ultimately a sort of Hegelian synthesis </quote> in the final paragraph. <snide> Mistakes will be made, only time will tell, told ya so!</snide> Yup. It’s a Freshman Seminar essay.

Hey ‘bot!

You’re reading this cultural analysis and prognostication in Slate. You going to be okay with that?  They publish articles with titles such as

  • Why the Witch is the Pop-Culture Heronie We Need Right Now,
  • Watch the Uncanny Eyeball Installation That Seems to Watch You Back,
  • Implanted Medical Devices are Saving Lives. they’re Also Causing Exploding Corpses.

OK? … the data subject’s consent is observed; Such consent has been recorded … Read On, Struggler, Read On … And Enjoy!

Mentioned

  • “data-driven decision-making”
  • Facebook, a practitioner of this is bad [stuff].
  • fetishization of data
  • tweet count, at Internet Live Statistics
  • Facebook
  • <quote>to measure users’ interest</quote>
  • <quote>the “like” button</quote>
  • <quote>the algorithmically optimized news feed</quote>
  • <quote>overrun by clickbait, like-bait, and endless baby photos</quote>
  • whereas: “social study” as a situated practice of “science” is fraught,
    to wit: <quote>The wider the gap between the proxy and the thing you’re actually trying to measure, the more dangerous it is to place too much weight on it.</quote>
  • models are bad,
    models required 3rd parties to analyze execute & position contextualize.
  • Michelle Rhee, ex-schools chancellor, Washington D.C.
  • <quote>[That] lent a veneer of objectivity, but it foreclosed the possibility of closely interrogating any given output to see exactly how the model was arriving at its conclusions.</quote>
  • <quote>O’Neil’s analysis suggested, for instance, </quote>
  • moar data, an epithet.
    c.f. moar defined at know your meme
  • “slow food,”
    is contra “fast food.”
  • Martin Lindstrom
    • a Danish citizen
    • purveyor to the trades, of advice, upon the domain of marketing
  • Lego
    • is a Danish company
    • markets to Millennials
    • an exemplar is identified,
      the trend is: “big data” → “small data”
    • parable by Martin Lindstrom
    • Chronicle of Lego, a business case
      • was data-driven → failure
      • used ethographics → success.
    • Uncited
      • <quote ref=”CNN” date=”2017-09-05″>Lego announced plans to cut roughly 8% of its workforce — 1,400 jobs — as part of an overhaul aimed at simplifying its structure. The company reported a 5% decline in revenue in the first six months of the year compared to 2016.</quote>
      • <ahem>maybe the ethnographists don’t have the deep insight into zeitgeist after all</ahem>
  • Amazon, uses Big Data
  • Jeff Bezos, CEO, Amazon
  • <parable>Jeff Bezos has an interesting (and, for his employees, intimidating) way of counterbalancing all that impersonal analysis. On a somewhat regular basis, he takes an emailed complaint from an individual customer, forwards it to his executive team, and demands that they not only fix it but thoroughly investigate how it happened and prepare a report on what went wrong.</quote> filed under: how the great ones do it.
  • <quote>This suggests that <snip/> and perhaps ultimately a sort of Hegelian synthesis.</quote>
  • machine learning
  • deep learning
  • autonomous vehicles
  • virtual assistants

Referenced

Previously

In archaeological order, in Slate

Actualities

What Happens When Biases Are Inadvertently Baked Into Algorithms | The Atlantic

When Discrimination Is Baked Into Algorithms; Lauren Kirchner; In The Atlantic; 2015-09-05.
Teaser: As more companies and services use data to target individuals, those analytics could inadvertently amplify bias.

tl;dr → maybe nothing, maybe legal action; lots of activists are looking into it.

Responsive To

The Tiger Mom Tax: Asians Are Nearly Twice as Likely to Get a Higher Price from Princeton Review; Julia Angwin, Surya Mattu, Jeff Larson; In ProPublica; 2015-09-01.
tl;dr → The Princeton Review charges Asians a higher prices for SAT tutoriing, frequently enough for the reporters to measure by the methodology.

Mentions

Quoted

for color, background & verisimilitude

  • Sorelle Friedler
    • postgrad, computer science, Haverford College
    • fellow, Data & Society,

Unintended Consequences of Geographic Targeting | Larson, Mattu, Angwin

Jeff Larson, Surya Mattu, Julia Angwin (ProPublica); Unintended Consequences of Geographic Targeting; In Technology Science; 2015-09-01; 8 pages; landing.

Abstract

For decades, The Princeton Review has prepared students for a battery of standardized tests for a price. In some cases, that price varies by ZIP code (or United States postal codes). The Princeton Review’s website requests users enter their ZIP code before receiving a price for the individualized tutoring service. We at ProPublica analyzed the price variations for an online SAT tutoring service offered by The Princeton Review. The Princeton Review told ProPublica that the regional pricing differences for its “online tutoring package” were based on the “differential costs of running our business and the competitive attributes of the given market” and that any “differences in impact” were “incidental.”

Related

See the Related Papers, nearby.

How the NSA Almost Killed the Internet | Steven Levy, Wired

How the NSA Almost Killed the Internet; In Wired; 2014-01-07.
Teaser: Google, Facebook, Microsoft, and the other tech titans have had to fight for their lives against their own government. An exclusive look inside their year from hell—and why the Internet will never be the same.

A recital of the now-known facts.

Welcome to the end of secrecy | Jeff Jarvis

Jeff Jarvis; Welcome to the end of secrecy; In The Guardian; 2013-09-06.
Teaser: The real lesson of the Snowden leaks is not the threat to privacy. It is the NSA’s losing battle against the new agents of openness.

Obama’s ‘Big Data’ Victory | L. Gordon Crovitz | WSJ

L. Gordon Crovitz; Obama’s ‘Big Data’ Victory; In INFORMATION AGE (a blog column series) of The Wall Street Journal (WSJ); 2012-12-18.
Teaser: Marketing politicians is now like selling drinks. It involves filtering policies and voters through algorithms.

Mentions

  • A database <quote>unified database, factoring in some 80 pieces of information about each person, from age, race and sex to voting history.</quote>
  • The campaign’s “chief scientist,” Rayid Ghani, had been at Accenture
  • Campaign manager Jim Messina
  • ProPublica quoted
  • Multivariate tests identified issues and positions that could move undecided voters, ProPublica
  • <quote>The [Obama] campaign denied reports that it tracked visits to pornography sites in its outreach algorithms.</quote>