Code Dependent: Pros and Cons of the Algorithm Age | Pew Research

, ; Code Dependent: Pros and Cons of the Algorithm Age; 2017-02-08; 87 pages; landing.
Teaser: Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment.

tl;dr → there be dragons; this is an important area; the future is at stake; the alarum has been sounded; there are seers who can show us the way. In their own words.


Future of the Internet, of Pew Research & Elon University.

Table of Contents

  • Overview
  • Themes illuminating concerns and challenges
  • Key experts’ thinking about the future impacts of algorithms
  • About this canvassing of experts
  • Theme 1: Algorithms will continue to spread everywhere
  • Theme 2: Good things lie ahead
  • Theme 3: Humanity and human judgment are lost when data and predictive modeling become paramount
  • Theme 4: Biases exist in algorithmically-organized systems
  • Theme 5: Algorithmic categorizations deepen divides
  • Theme 6: Unemployment will rise
  • Theme 7: The need grows for algorithmic literacy, transparency and oversight
  • Acknowledgments


Code-Dependent: Pros and Cons of the Algorithm Age; , (Pew Research Center); In Their Blog; 2017-02-08.

Teaser: Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment/


  • Pew Research Center of the Pew Charitable Trusts
  • Imagining the Internet Center at Elon Univesity
  • <ahem>the Singularity enthusiasts … .</ahem>


  1. Algorithms will continue to spread everywhere
  2. Good things lie ahead
  3. Humanity adn human judgement are lost wwhen data nad predictive modeling become paramount
  4. Biases exist in algorithymically-organized systems
  5. algorithmic categorizations deepen divides
  6. Unemployment will rise
  7. The need grows for algorithmic literacy, transparency and oversight.


  • <snicker>Artificial Intelligence (AI)</snicker>
  • algocratic governance
  • surveillance capitalism
  • information capitalism
  • topsight
  • black-box nature [of]
  • digital scientism
  • obedience score


  • Aneesh Aneesh, Stanford University.
  • Peter Diamandis, CEO, XPrize Foundation.
  • Shoshana Zuboff, Harvard.
  • Jim Warren, activist.
  • Terry Langendoen, expert, U.S. National Science Foundation.
  • Patrick Tucker technology editor at Defense One,.
  • Paul Jones, clinical professor at the University of North Carolina-Chapel Hill and director of
  • David Krieger, director of the Institute for Communication & Leadership IKF,.
  • Galen Hunt, partner research manager at Microsoft Research NExT,.
  • Alf Rehn, professor and chair of management and organization at Åbo Akademi University in Finland,.
  • Andrew Nachison, founder at We Media,.
  • Luis Lach, president of the Sociedad Mexicana de Computación en la Educación, A.C.
  • Frank Pasquale, professor of law, University of Maryland.
  • Jeff Jarvis, reporter.
  • Cindy Cohn, executive director at the Electronic Frontier Foundation,.
  • Bernardo A. Huberman, senior fellow and director of the Mechanisms and Design Lab at HPE Labs, Hewlett Packard Enterprise.
  • Marcel bullinga, expert.
  • Michael Rogers, principal, Practical Futurist.
  • Brian Christian, Tom Griffiths.
  • David Gelertner.
  • Deloitte Global (anonymous contributors).
  • Barry Chudakov, founder and principal at Sertain Research and StreamFuzion Corp.
  • Stephen Downes, staff, National Research Council of Canada,.
  • Bart Knijnenburg, assistant professor in human-centered computing at Clemson University.
  • Justin Reich, executive director at the MIT Teaching Systems Lab.
  • Dudley Irish, tradesman (a coder).
  • Ryan Hayes, owner of Fit to Tweet,.
  • Adam Gismondi, a visiting scholar at Boston College.
  • Susan Etlinger, staff, Altimeter Group.
  • Chris Kutarna, fellow, Oxford Martin School.
  • Vintno Cert, Internet Hall of Fame, vice president and chief internet evangelist at Google:.
  • Cory Doctorow, writer, computer science activist-in-residence at MIT Media Lab and co-owner of Boing Boing.
  • Jonathan Grudin, Microsoft.
  • Doc Searls, director, Project VRM, Berkman Center, Harvard University,.
  • Marc Rotenberg, executive director of the Electronic Privacy Information Center.
  • Richard Stallman, Internet Hall of Fame, president of the Free Software Foundation.
  • David Clark, Internet Hall of Fame, senior research scientist at MIT,.
  • Baratunde Thurston, Director’s Fellow at MIT Media Lab, ex-digital director of The Onion.
  • Anil Dash, pundit.
  • John Markoff, New York Times.
  • Danah Boyd (“danah boyd”), founder, Data & Society, an advocacy group.
  • Henning Schulzrinne, Internet Hall of Fame, professor at Columbia University,.
  • Amy Webb, futurist and CEO at the Future Today Institute.
  • Jamais Cascio, distinguished fellow at the Institute for the Future.
  • Mike Liebhold, senior researcher and distinguished fellow at the Institute for the Future,.
  • Ben Shneiderman, professor of computer science at the University of Maryland,.
  • David Weinberger, senior researcher at the Harvard Berkman Klein Center for Internet & Society.


Previously filled.

Silicon’s Valley’s Brutal Ageism | New Republic

Silicon’s Valley’s Brutal Ageism; Noam Scheiber; In New Republic; 2014-03-23.


Roughly in order of appearance, the article meanders to tell the story.

  • Seth Matarasso
    • A cosmetic surgeon
    • Practice in San Francisco
  • Robert Withers
    • A job search counselor
    • Clients are Silicon Valley workers over 40
    • Advice
      • Look, act, dress young
      • Show energy
  • Dan Scheinman
    • An angel investor
    • Tango, an investment; recent press cycling [Alibaba]
    • ex-Cisco, 18 years
    • Presentation Bias [to the young, away from the old]
  • Illustrative
    • “We Want People Who Have Their Best Work Ahead of Them, Not Behind Them.”  ServiceNow Careers
    • “You must be the token graybeard,” said the CEO, who was in his late twenties or early thirties. “I looked at him and said, ‘No, I’m the token grown-up.’ ” anonymous source, engineer, aged 40.
    • “Paul Graham”—the founder of Y Combinator, the world’s best-known start-up incubator—“says the most successful [investor] makes his decisions in twenty-four hours.” attributed to Scheinman
  • Nick Stamos
    • age 44
    • Phase Forward
    • nCrypted Cloud
      • founder
      • like Dropbox and Google Drive.
      • Competitor:
      • With Dan Scheinman
      • Needed Sand Hill funding, was declined
      • Claim: Total Addressable Market (TAM) => transferrence from something else [age]
      • Long River Ventures (MA); Acquired funding? or just a lead.
      • [Mr.] Aref; a lead, declined.
    • Prior
      • serial entrepreneur, various
      • ex-Tufts (graduate)
      • ex-Lotus,
      • ex-Verdasys, 2002
  • Generalized Themes & Mentions
  • Dropbox
    • Drew Houston, co-founder, ex-MIT (graduate)
    • founded 2007
  • Google
    • Brian Reid, age 54, age discrimination litigation
  • Freada Klein
    • 1999, starts a 5-year “quality of work study”
    • 22 startups
    • Result: older candidates held to a higher standard
  • Culture
    • Interviewing for “fit”
    • “a college atmosphere”
    • “disruption”
  • Quoted for Background, Verisimilitude & Color
  • Criticism
    • <quote>Unfortunately, the problems the average 22-year-old male programmer has experienced are all about being an affluent single guy in Northern California.</quote>
  • Outbox
  • Mark Goldenson
    • serial entrepreneur
    • age 30
    • founded Breakthrough
    • recommender system for psychiatrist counseling lead generation online
  • Evidentiary

Referenced, Related

  • Dane Stangler, with Daniel F. Spulber; The Age of the Entrepreneur: Demographics and Entrepreneurship; I4J Summit; 2013-03; 27 pages.
  • Moneyball, valley-style: Investor uses age bias to advantage, funds older entrepreneurs; Sarah McBride (Reuters); In San Jose Mercury News; 2012-12-07.
  • Special Report: Silicon Valley’s dirty secret – age bias; Sarah McBride; In Reuters; 2012-11-27.

    • <quote>When Randy Adams, 60, was looking for a chief-executive officer job in Silicon Valley last year, he got turned down from position after position that he thought he was going to nail — only to see much younger, less-experienced men win out. Finally, before heading into his next interview, he shaved off his gray hair and traded in his loafers for a pair of Converse sneakers. The board hired him.</quote>
    • “[The young prospects] They have great passion. They don’t have distractions like families and children and other things that get in the way of business.” attributed to Mike Moritz, when age 49 [9 years ago].
    • “Young people are just smarter.” Mark Zuckerberg at age 22.
    • <quote>In June (2012-06), Benchmark Capital’s Peter Fenton, 40, told a group of journalists that Benchmark strives to keep the average age of its most-active partners under 40 to better relate to young entrepreneurs. Fenton says he is not ageist, arguing that there is a well-documented relationship between youth and creativity. As for partners such as himself who hit 40, “we have a discipline to try and stay young,” he says. “Young at mind.”</quote>
    • Something about Fluid Intelligence; c.f. Jimi Wales’ Wiki
      • Fluid Intelligence (Gf)
      • Crystallized Intelligence (Gc)
    • Advice on how to dress & accessorize “as young”

      • show as hipster: tights, flannels, Converse, boots.
      • avoid and/or fix: gray hair, fat
      • use: Android+Apple contra Dell+Blackberry; avoid wristwatches, especially gold.
  • Education and Tech Entrepreneurship; Vivek Wadhwa, Richard Freeman, Ben Rissing; Ewing Marion Kauffman Foundation; 2008-05; 16 pages; promotion 2009-04-17; landing.

Via: backfill