Toward a critical theory of corporate wellness | Gordon Hull & Frank Pasquale

Gordon Hull; More self-promotion: “Toward a critical theory of corporate wellness”; In His Blog; 2017-07-10.

tl;dr → employee wellness is but a surveillance-cum-control plane; a promotion of a forthcoming paper (you can read it for free if you’ve paid, which is a paywall)

Original Sources

Gordon Hull, Frank Pasquale; “Toward a critical theory of corporate wellness,”  In Biosocieties; soon.paywall.



In the U.S., “employee wellness” programs are increasingly attached to employer-provided health insurance. These programs attempt to nudge employees, sometimes quite forcefully, into healthy behaviors such as smoking cessation and exercise routines. Despite being widely promoted as saving on healthcare costs, numerous studies undermine this rationale. After documenting the programs’ failure to deliver a positive return on investment, we analyze them as instead providing an opportunity for employers to exercise increasing control over their employees. Based on human capital theory and neoliberal models of subjectivity that emphasize personal control and responsibility, these programs treat wellness as a lifestyle that employees must be cajoled into adopting, extending the workplace not just into the home but into the bodies of workers and entrenching the view that one belongs to one’s workplace. At the same time, their selective endorsement of health programs (many scientifically unsupported) produce a social truth of wellness framed as fitness for work. We conclude by arguing that the public health initiatives occluded by the private sector’s promotion of wellness programs would be a much better investment of resources.


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A Memo From MMT’s Legal Department | naked capitalism (New Economic Perspectives)

A Memo From MMT’s Legal Department; ; In naked capitalism, syndicated from New Economic Perspectives; 2017-07-18.
Devin Smith, staff, an economist, Corps of Engineers, United States Army.


  • 1st International MMT Congress, 2017-09
    to be held at the University of Missouri-Kansas City.
  • A recital of
    • affiliated persons
    • promotional events


  • Modern Monetary Theory (MMT)
  • Job Guarantee
  • Tax obligations create money
  • Tax obligations require wages.
  • Modern Money Network
    • founded 2012
  • Federal Reserve System
  • Rethinking Economics New York
    • a conference
    • Student-organized
    • Sponsors
      • Law School, Columbia University
      • Law School, New York University
      • Economics Department, The New School
  • Association for the Promotion of Political Economy and Law (APPEAL)


Has a moneyness sense

Treasury currency
Commodity Futures Trading Commission commodity
Internal Revenue Service property


<quote>“[A] renewed emphasis on a key legal insight (the government cannot default on debt it issues in its own currency) can lead to an adjustment to economic theory (MMT), which in turn informs a new legal proposal to get past the current, futile […] debate.
It’s a movement from legal insight to economic insight back to legal insight, or L – E – L.
… The legal foundations of MMT make it both scientifically, and normatively, a better theory of our economic system than the dominant paradigms of monetary policy.”</quote>, attributed to Frank Pasquale, circa 2014.

<quote>“Modern money theory explains that political and legal systems for creating, regulating, and distributing money are fundamental to economic prosperity and stability, necessarily shaping (not “distorting”) and facilitating private exchanges of goods and services.
… the economic costs and benefits of public money creation depend on the contingent, complex value-laden questions of how that money is spent and invested and how effectively taxes and other forms of regulation help steer economic and political activity toward the productivity, stability, and legitimacy that will help maintain currency value.”</quote>, attributed to Martha McCluskey, circa 2016.


  • Rania Antonopoulos, Alternate Minister for Combatting Unemployment, Greece
  • Marshall Auerback
  • David Bholat, staff, Advanced Analytics Division, Bank of England
  • Richard Clarida, (former) Assistant Secretary, U.S. Treasury
  • Mathew Forstater
  • James K. Galbraith
  • Philip Harvey
    • Professor, Law, Rutgers University
    • (author) Rep. John Conyers H.R. 1000 towards full employment
      Versions: 114th Congress and many others
  • NAME Innes
  • Stephanie Kelton
  • NAME Knapp
  • NAME Keynes (surely you jeste)
  • Martha McCluskey, Professor, Law, University at Buffalo
  • Frank Pasquale, Professor, Law, University of Maryland
  • Zoltan Pozsar, (former) Senior Advisor, U.S. Treasury Department
  • Amar Reganti, ex-Deputy Director, Office of Debt Management at United States Treasury
  • Beardsley Ruml, Chairman of the Federal Reserve Bank of New York, once upon a time (at least in 1946-01)
  • Joseph Sommer, Legal Counsel, New York Federal Reserve Bank
  • Pavlina Tcherneva
  • Alexis Tsipras, (now-)Prime Minister, Greece,
  • Adair Turner, (former) Chairman, U.K. Financial Services Authority
  • Matias Vernengo, (former) Economic Research Director, Central Bank of Argentina.
  • Randy Wray


The Princeton Web Transparency And Accountability Project | Narayanan, Reisman

Arvind Narayanan, Dillon Reisman; The Princeton Web Transparency and Accountability Project; In Tania Cerquitelli, Daniele Quercia, Frank Pasquale (editors); Transparent Data Mining for Big and Small Data; Springer; 2017.

tl;dr → There be dragons. Princeton was is there. Tell it! Testify!


When you browse the web, hidden “third parties” collect a large amount of data about your behavior. This data feeds algorithms to target ads to you, tailor your news recommendations, and sometimes vary prices of online products. The network of trackers comprises hundreds of entities, but consumers have little awareness of its pervasiveness and sophistication. This chapter discusses the findings and experiences of the Princeton Web Transparency Project, which continually monitors the web to uncover what user data companies collect, how they collect it, and what they do with it. We do this via a largely automated monthly “census” of the top 1 million websites, in effect “tracking the trackers”. Our tools and findings have proven useful to regulators and investigatory journalists, and have led to greater public awareness, the cessation of some privacy-infringing practices, and the creation of new consumer privacy tools. But the work raises many new questions. For example, should we hold websites accountable for the privacy breaches caused by third parties? The chapter concludes with a discussion of such tricky issues and makes recommendations for public policy and regulation of privacy.


  • Marvin Minsky
  • expert systems
  • Machine Learning
  • Artifical Intelligence
  • Big Data
  • Netflix
  • Self-Driving Cars
  • collect data first, ask questions later
  • surveillance infrastructure
  • Kafkaesque
  • data and algorithmic transparency
  • Workshop on Data and Algorithmic Transparency
  • Princeton Web Transparency and Accountability Project (WebTAP)
    Princeton Web Census
  • Privacy scholar
  • Ryan Calo
  • The Party System
  • first party
  • third party
  • Twitter
  • Facebook
  • Facebook Like Button
  • The Beauty and the Beast Project
  • Panopticlick
  • Anonymous
  • Pseudonymous
  • biases
  • discrimination
  • targeted political messaging
  • price discrimination
  • market manipulation
  • AdChoices
  • ad blockers
  • Federal Trade Commission (FTC)
  • Optimizely
  • A/B Testing
  • OpenWPM (Open Web Privacy Measurement)
  • FourthParth
  • FPDetective
  • PhamtomJS
  • Firefox
  • Tor
  • Facebook Connect
  • Google Single Sign-On (SSO)
  • longitudinal studies
  • HTML5, Canvas API
  • canvas fingerprinting
  • AddThis
  • AudioContext API
  • WebRTC API
  • Battery Status API
  • NSA (National Security Agency)
  • Snowden
  • Cookies
  • transitive cookie linking
  • cookie syncing
  • Google
  • Facebook
  • Federal Trade Commission (FTC)
  • Cross-Device Tracking
  • header enrichment (by ISPs)
  • Ghostery
  • AdBlock Plus
  • uBlock Origin
  • machine learning classifier (for tracking behavior)
  • Big Data (they used Big Data and Machine Learning Classifiers)
  • Nudge (a book)
  • Choice Architecture
  • 3rd Part Cookies, blocking 3rd party cookies
  • Do Not Track
  • Battery API
  • Internet Explorer
  • zero sum game
  • power user interfaces
  • PGP (Pretty Good Privacy)
  • Cookie Blocking
  • <buzz>long tail (of innovation)</buzz>
  • Children’s Online Privacy Protection Act (COPPA)
  • child-directed websites.
  • American Civil Liberties Union (ACLU)
  • Computer Fraud and Abuse Act
  • Personally-Identifiable Information (PII)
  • shift of power, from 3rd parties to publishers
  • Columbia University
  • Carnegie Mellon University
  • Internet of Things (IoT)
  • WiFi
  • cross-device tracking
  • smartphone app
  • Fairness, Accountability and Transparency in Machine Learning (FAT-ML)
  • Princeton


  • “The best minds of our generation are thinking about how to make people click on ads” attributed to Jeff Hammerbacher


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Big Data Tells Mortgage Traders an Amazing Amount About You | Bloomberg

Big Data Tells Mortgage Traders an Amazing Amount About You; Matt Scully; In Bloomberg; 2017-06-29.

  • Wall Street startup gives mortgage bond traders vivid data
  • Consumer rights watchdogs raise concerns about privacy



  • New York
  • Founders
    • Hans Thomas
    • Guhan Kandasamy
    • Ziggy Jonsson
  • Founded 2015


  • Claim: <quote>The average fund manager can gain 0.40 to 0.70 percentage point of return by using more intelligent data when trading mortgages<snip/></quote>
  • <quote>Regulators could conceivably deem TheNumber a consumer credit bureau like TransUnion or Experian Plc.</quote>
  • Propensity Scoring… ,br/><quote>TheNumber tries to determine how much pride a homeowner probably has in his or her property, based on information it gleans from third parties, such as whether the resident tends to click on online ads from home improvement and gardening stores. It’s not a credit score, but<snip/></quote>
  • Mortgate (financialization & repackaging) Regulation
    Reg AB II, SEC
    <quote>ended up requiring less information from issuers than it had originally planned, to protect borrowers’ identities.</quote>
  • (no reference given) An RFC; “The Consumer Financial Protection Bureau”; 2017-02.
    solicitation: <quote>comments about the benefits, and risks, of using alternative data.</quote>


In order of appearance

  • John Ardy, chief executive officer Resitrader (a mortgage resale market)
  • Lee Tien, a senior staff attorney, Electronic Frontier Foundation (EFF)
  • Frank Pasquale, professor, Francis King Carey School of Law, University of Maryland.
  • Jeff Taft, partner, Mayer Brown (legal advice)
  • Michael Osnato, ex-”head” of “an enforcement unit”, Securities and Exchange Commission (SEC)
  • Michele Raneri, (a) vice president of analytics and new business development, Experian
  • Paul Mangione, now-consultant, ex-staff, Apollo Residential Mortgage. Mangione
  • Brian Tortorella, staff, Smith Graham (mortgage trading, arbitrage).
  • Adam Murphy, founder, Empirasign Strategies LLC (mortgage trading & data)


In Bloomberg

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.



for color, background & verisimilitude

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

The Scoreboards Where You Can’t See Your Score | NYT

The Scoreboards Where You Can’t See Your Score; Natasha Singer In The New York Times (NYT); 2014-12-28.

tl;dr => two book promos for books appearing 2015-01.