Attribution by Design, a meta-promotion



The Evolving Data Landscape: Veracity, Convergence And Anonymity | Ad Exchanger

Ramsey McGrory (Mediaocean); The Evolving Data Landscape: Veracity, Convergence And Anonymity; In AdExchanger; 2017-09-21.
Ramsey McGrory, chief revenue officer at Mediaocean

tl;dr → something about accuracy of imputations in consumer profiles, accuracy of “data.”

Original Sources

Ramsey McGrory (AddThis); The Data Providers One Quadrant Chart To Rule Them All; 2013-02.
tl;dr → it’s a metaphor with four (4) quadrants induced by a 2-axis “system”; later a 3rd access, a Z-axis

  1. online ↔ offline
  2. anonymous → personal
  3. singleton → conglomerate


  • <quote>data being neither intrinsically “good” nor “bad,” but rather having “qualities.”<quote>, attributed to Ted McConnell.
  • behaviors, drive actions.
  • Viewability
  • Verification
  • Something allegorical about Viewability and Trust & Safety vending as a separable service of attestation, 2012 → 2017.
  • <quote>Viewability speaks to a broader metadata theme of trust, as well as an underlying theme of data quality and users’ engagement with content delivered against this data.</quote>
  • Hey! That’s not a business, that’s a Business Unit;
    Hey! That’s not a BU, that’s a Product.
    Hey! That’s not a Product, that’s a Feature.
    <quote>Then, these vertical standalone organizations and solutions were horizontally integrated into the operating agencies as capabilities.</quote>


  • SafeGraph <quote>works with universities and health organizations to understand movement data and the spread of infectious diseases.</quote>
  • [all] device IDs are persistent
  • <quote>there are growing trends toward people taking control of their anonymization through the use of virtual private networks and Tor</quote>
    • As stated:
      • casual consumer use of VPNs is prevalent [enough to measure]
      • casual consumer use of Tor is prevalent [enough to measure]
    • Contrast with:
      <surely>IPv6 use is prevalent,
      IPv6 use is prevalent enough to warrant dual-stack interfaces on the great centralized ad exchanges.</surely>
  • <quote>mobile, where cookies can’t be used</quote>
  • <quote>that major brands may view agencies as differentiated commodity services, put their media in review with greater frequency and bid them down.</quote>
  • The adtech bubble is ongoing; adtech will be forward-funded on an ongoing basis:<quote><snip/> will continue to be funded with massive capital because the opportunities for innovation and disruption are huge.</quote>


Three Four V’s of Data
  1. volume
  2. velocity
  3. variety
  4. veracity


Big (conglomerates)
  • Adobe
  • Amazon
  • Google
  • IBM
  • Oracle
  • Salesforce
  • SAP
Cross-Device Fingerprinting
  • AdSafe
  • comScore
  • DoubleVerify
  • Moat
  • Amino
  • White Ops
Data Brokers
  • Experian
  • Acxiom
  • TransUnion
  • Equifax
Trading Desks
  • Xaxis of WPP
  • Nerve Center of VivaKi of Publicis
Data Breached
  • Yahoo
  • Equifax

Hearts & Science
<honorific>won major accounts on a transparent, data-centric and deeply integrated vision.</honorific>


  • Ted McConnell, practitioner.
    <quote>Ted McConnell, an independent consultant in the digital marketing space.</quote>



In Ad Exchanger


The Suitecase Words
  • ”data truth”
  • “moat for data”
  • “truth of the inference”
  • intenders, as “auto intenders”
  • attitudes
  • demographics
  • measurement
  • cross-device,
    cross-device mapping.
  • deterministic
  • Television
    • connected television
    • addressable television
    • advanced television
    • data-enabled television
    • targeted television
    • integrated television and video,
      integrated television and video initiatives
  • strategic elements
    strategic elements of advertising campaigns.
  • holistic planning
  • anonymous data
  • digital data
  • ad block
  • cookie block
  • mobile,
    growth of mobile.
  • device IDs
  • persistent
  • anonymity,
    desire for anonymity.
  • breaches,
    data breaches,
    massive data breaches

    • Yahoo
    • Equifax
  • sensitive information
    • Social Security numbers
    • birthdates
    • credit card numbers
  • collaboration
  • competition
  • companies,
    services companies,
    technology and services companies
  • space,
    media space.
  • to verb… with large agencies
    • partner
    • coexist
    • compete
  • Z-axis
  • execute,
    acquisitively execute,
    aggressively and acquisitively execute,
    continue to aggressively and acquisitively execute,
    continue to aggressively and acquisitively execute on their strategies,
    continue to aggressively and acquisitively execute on their strategies to deliver on

    • infrastructure
    • data
    • services
  • agencies,
    holding company agencies.
  • solutions,
    data-driven solutions,
    converged, data-driven solutions.
  • vision,
    • transparent vision
    • data-centric vision
    • integrated vision,
      deeply integrated vision.
  • The side,
    • The downside
    • The upside
  • brands,
    major brands.
  • services,
    commodity services,
    differentiated commodity services,
    agencies as differentiated commodity services.
  • themes
    • convergence
    • data activation
    • people
  • change,
    great change,
    in a time of such great change,
    wait for it … wait for it … the only constant is change …thank you, thank you very much, I’ll be here all week.
  • The Bottom Line
  • In a world of…
  • adjectivedata,
    • first-party data
    • third-party data
    • personal data
    • census data
    • anonymous data
    • panel data,
      <mmmmm>…panel data..…</mmmmm>
    • pixel data
  • understanding,
    deeper understanding,
    deeper understanding of consumers’ …
    deeper understanding of consumers’ awareness and interests,
    deeper understanding of consumers’ awareness and interests while enjoying <snip/> profitability,
    deeper understanding of consumers’ awareness and interests while enjoying short- and long-term profitability,
    deeper understanding of consumers’ awareness and interests while enjoying short- and long-term profitability of their brands.
  • vision,
    this vision,
    delivering on this vision.
  • infrastructure,
    data infrastructure,
    extensive data infrastructure.
  • understanding,
    deep understanding,
    deep understanding of

    • advertising
    • publishing,
      media publishing
    • ecommerce
  • ecosystems,
    technology ecosystems,

    • advertising technology ecosystems
    • marketing technology ecosystems
    • content technology ecosystems
  • ecosystems,
    the ecosystems,
    all the ecosystems,
    And across all the ecosystems
  • nounof data
    • consolidation of data
    • standardization of data
    • interpretation of data
    • activation
  • winners
    winners and losers
    winners and losers will be decided.
  • transformation,
    massive transformation,
    enable massive transformation,
    enable massive transformation at <snip/> lower costs.
    enable massive transformation at materially lower costs.

Situating Methods in the Magic of Big Data and Artificial Intelligence | Elish, Boyd

M. C. Elish (Columbia); danah boyd (Microsoft); Situating Methods in the Magic of Big Data and Artificial Intelligence; In Communication Monographs, Forthcoming; 2017-09-21; 30 pages; ssrn:3040201

tl;dr → they’re doing it wrong; an explainer.
and → <quote>we problematize the myths that animate the supposed “magic” of these systems</quote>


“Big Data” and “artificial intelligence” have captured the public imagination and are profoundly shaping social, economic, and political spheres. Through an interrogation of the histories, perceptions, and practices that shape these technologies, we problematize the myths that animate the supposed “magic” of these systems. In the face of an increasingly widespread blind faith in data-driven technologies, we argue for grounding machine learning-based practices and untethering them from hype and fear cycles. One path forward is to develop a rich methodological framework for addressing the strengths and weaknesses of doing data analysis. Through provocatively reimagining machine learning as computational ethnography, we invite practitioners to prioritize methodological reflection and recognize that all knowledge work is situated practice.


The Suitcase Words

Indeed.  Let’s see if the ‘bot can pick ‘em out.. The vein is deep and wide here.

Positive Truth

interrogation, problematize, myth, data-driven (empirical), grounding, practices, untethering, methodological framework, analysis, reimaginatively, provocatively reimagining, ethnography, computational ethnography, practitioners, reflection, methodological reflection, practice, situated practice, methods, situated methods.

Negative Truth (Stop Words)

and, have, the, an, of, that, these, we, in, an, for, from, one, is, to, a, through, as, we, all.

The Undue Influence of Surveillance Technology Companies on Policing | Elizabeth Joh

Elizabeth E. Joh; The Undue Influence of Surveillance Technology Companies on Policing; In Law Review Online, New York University (NYU); 2017-09; N pages; landing.
Elizabeth E. Joh is Professor of Law, School of Law, U.C. Davis.


Conventional wisdom assumes that the police are in control of their investigative tools. But with surveillance technologies, this is not always the case. Increasingly, police departments are consumers of surveillance technologies that are created, sold, and controlled by private companies. These surveillance technology companies exercise an undue influence over the police today in ways that aren’t widely acknowledged, but that have enormous consequences for civil liberties and police oversight. Three seemingly unrelated examples—stingray cellphone surveillance, body cameras, and big data software—demonstrate varieties of this undue influence. The companies which provide these technologies act out of private self-interest, but their decisions have considerable public impact. The harms of this private influence include the distortion of Fourth Amendment law, the undermining of accountability by design, and the erosion of transparency norms. This Essay demonstrates the increasing degree to which surveillance technology vendors can guide, shape, and limit policing in ways that are not widely recognized. Any vision of increased police accountability today cannot be complete without consideration of the role surveillance technology companies play.


    1. Stingray Cellphone Surveillance and Nondisclosure Agreements
      1. Nondisclosure Agreements
      2. Stingrays and the Fourth Amendment
      3. Secret Stingray Use
    2. Cornering the Market on Police Body Cameras
      1. When Product Design Is Policy
      2. Market Dominance
    3. Big Data Software and Proprietary Information
    1. Fourth Amendment Distortion
    2. Accountability by Design
    3. Outsourcing Suspicion and Obscuring Transparency
    1. Local Surveillance Oversight
    2. Public Records Requests as Oversight

Toward a Fourth Law of Robotics: Preserving Attribution, Responsibility, and Explainability in an Algorithmic Society | Pasquale

Frank A. Pasquale III; Toward a Fourth Law of Robotics: Preserving Attribution, Responsibility, and Explainability in an Algorithmic Society; Ohio State Law Journal, Vol. 78, 2017, U of Maryland Legal Studies Research Paper No. 2017-21; 2017-07-14; 13 pages; ssrn:3002546.

tl;dr → A comment for Balkin. To wit:
  1. Balkin should have supplied more context; such correction is supplied herewith.
  2. More expansive supervision is indicated; such expansion is supplied herewith.
  3. Another law is warranted; not a trinity, but perfection plus one more

Four Laws, here and previous:

  1. machine operators are always responsible for their machines.
  2. businesses are always responsible for their operators.
  3. machines must not pollute.
  4. A [machine] must always indicate the identity of its creator, controller, or owner.

Love the erudition; but this is just like planes, trains & automobiles.

Separately noted.

The Three Laws of Robotics in the Age of Big Data | Balkin

Jack M. Balkin  (Yale); The Three Laws of Robotics in the Age of Big Data; Ohio State Law Journal, Vol. 78, (2017), Forthcoming (real soon now, RSN), Yale Law School, Public Law Research Paper No. 592; 2016-12-29 → 2017-09-10; 45 pages; ssrn:2890965.

tl;dr → administrative laws [should be] directed at human beings and human organizations, not at [machines].


  1. machine operators are responsible
    [for the operations of their machines, always & everywhere]
  2. businesses are responsible
    [for the operation of their machines, always & everywhere]
  3. machines must not pollute
    [in a sense to be defined later: e.g. by a "tussle"]

Love the erudition; but none of this is new.

Separately noted.

Big Data Surveillance: The Case of Policing | Sarah Brayne


This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities.

  1. First, discretionary assessments of risk are supplemented and quantified using risk scores.
  2. Second, data are used for predictive, rather than reactive or explanatory, purposes.
  3. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people.
  4. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact.
  5. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions.

Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.


Through a case study of the Los Angeles Police Department, this article analyzed the role of big data in surveillance practices. By socially situating big data, I examined why it was adopted, how it is used, and what the implications of its use are. Focusing on the interplay between surveillance practices, law, and technology offers new insights into social control and inequality. I argued that big data participates in and reflects existing social structures. Far from eliminating human discretion and bias, big data represents a new form of capital that is both a social product and a social resource. What data law enforcement collects, their methods for analyzing and interpreting it, and the way it informs their practice are all part of a fundamentally social process. Characterizing predictive models as “just math,” and fetishizing computation as an objective process, obscures the social side of algorithmic decision-making. Individuals’ interpretation of data occurs in preexisting institutional, legal, and social settings, and it is through that interpretive process that power dynamics come into play. Use of big data has the potential to ameliorate discriminatory practices, but these findings suggest implementation is of paramount importance. As organizational theory and literature from science and technology studies suggests, when new technology is overlaid onto an old organizational structure, longstanding problems shape themselves to the contours of the new technology, and new unintended consequences are generated. The process of transforming individual actions into “objective” data raises fundamentally sociological questions that this research only begins to address. In many ways, it transposes classic concerns from the sociology of quantification about simplification, decontextualization, and the privileging of measurable complex social phenomena onto the big data landscape. Surveillance is always ambiguous; it is implicated in both social inclusion and exclusion, and it creates both opportunities and constraints. The way in which surveillance helps achieve organizational goals and structure life chances may differ according to the individuals and institutions involved. Examining the means of big data surveillance across institutional domains is an open and timely line of inquiry, because once a new technology is disseminated in an institutional setting, it is difficult to scale back.


  • Palantir
    • PredPol
    • Automatic License Plate Reader (ALPR)
    • Automatic Vehicle Locator (AVL)
    • Enterprise Master Person Index (EMPI)
  • Los Angeles Police Department (LAPD)
  • Big Data
  • risk scores
  • Smart Policiing Initiative
  • Department of Homeland Security (DHS)
  • Federal Bureau of Investigations (FBI)
  • Central Intelligence Agency (CIA)
  • Immigration and Customs Enforcement (ICE)
  • Los Angeles County Sheriff’s Department (LASD)
  • Fusion Center
  • JRIC
  • Real-Time Crime Analysis Center (RACR)
  • Crime Intelligence Detail (CID)
  • Operation LASER (Los Angeles’ Strategic Extraction and Restoration
  • Parole Compliance Unit
  • Field Fnterview (FI)


  • surveillant assemblage
  • trigger mechanism
  • in the system (to be ‘in the system’)
  • net widening
  • decontextualization
(at least)
  • Deleuze
  • Guattari
  • Marx; no: not that guy; the other one.
  • Pasquale


intended for use in national defense, Palantir was initially partially funded by In-Q-Tel, the CIA’s venture capital firm. Palantir now has government and commercial customers, including the CIA, FBI, ICE, LAPD, NYPD, NSA, DHS, and J.P. Morgan. JRIC (the Southern California fusion center) started using Palantir in 2009, with the LAPD following shortly after. The use of Palantir has expanded rapidly through the Department, with regular training sessions and more divisions signing on each year. It has also spread throughout the greater L.A. region: in 2014, Palantir won the Request for Proposals to implement the statewide AB 109 administration program, which involves data integration and monitoring of the post-release community supervision population.</quote>

<quote>When I asked an officer to provide examples of why he stops people with high point values, he replied:

Yesterday this individual might have got stopped because he jaywalked. Today he mighta got stopped because he didn’t use his turn signal or whatever the case might be. So that’s two points . . . you could conduct an investigation or if something seems out of place you have your consensual stops.[7] So a pedestrian stop, this individual’s walking, “Hey, can I talk to you for a moment?” “Yeah what’s up?” You know, and then you just start filling out your card as he answers questions or whatever. And what it was telling us is who is out on the street, you know, who’s out there not neces- sarily maybe committing a crime but who’s active on the streets. You put the activity of . . . being in a street with maybe their violent background and one and one might create the next crime that’s gonna occur.



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Incompatible: The GDPR in the Age of Big Data | Tal Zarsky

Tal Zarsky (Haifa); Incompatible: The GDPR in the Age of Big Data; Seton Hall Law Review, Vol. 47, No. 4(2), 2017; 2017-08-22; 26 pages; ssrn:3022646.

tl;dr → the opposition is elucidated and juxtaposed; the domain is problematized.
and → “Big Data,” by definition, is opportunistic and unsupervisable; it collects everything and identifies something later in the backend.  Else it is not “Big Data” (it is “little data,” which is known, familiar, boring, and of course has settled law surrounding its operational envelope).


After years of drafting and negotiations, the EU finally passed the General Data Protection Regulation (GDPR). The GDPR’s impact will, most likely, be profound. Among the challenges data protection law faces in the digital age, the emergence of Big Data is perhaps the greatest. Indeed, Big Data analysis carries both hope and potential harm to the individuals whose data is analyzed, as well as other individuals indirectly affected by such analyses. These novel developments call for both conceptual and practical changes in the current legal setting.

Unfortunately, the GDPR fails to properly address the surge in Big Data practices. The GDPR’s provisions are — to borrow a key term used throughout EU data protection regulation — incompatible with the data environment that the availability of Big Data generates. Such incompatibility is destined to render many of the GDPR’s provisions quickly irrelevant. Alternatively, the GDPR’s enactment could substantially alter the way Big Data analysis is conducted, transferring it to one that is suboptimal and inefficient. It will do so while stalling innovation in Europe and limiting utility to European citizens, while not necessarily providing such citizens with greater privacy protection.

After a brief introduction (Part I), Part II quickly defines Big Data and its relevance to EU data protection law. Part III addresses four central concepts of EU data protection law as manifested in the GDPR: Purpose Specification, Data Minimization, Automated Decisions and Special Categories. It thereafter proceeds to demonstrate that the treatment of every one of these concepts in the GDPR is lacking and in fact incompatible with the prospects of Big Data analysis. Part IV concludes by discussing the aggregated effect of such incompatibilities on regulated entities, the EU, and society in general.


<snide>Apparently this was not known before the activists captured the legislature and affected their ends with the force of law. Now we know. Yet we all must obey the law, as it stands and as it is written. And why was this not published in an EU-located law journal, perhaps one located in … Brussels?</snide>


A. Purpose Limitation
B. Data Minimization
C. Special Categories
D. Automated Decisions


There are 123 references, manifested as footnotes in the legal style.

Separately noted.

The Spread of Mass Surveillance, 1995 to Present (Big Data Innovation Transfer and Governance in Emerging High Technology States) | CPS

Nadiya Kostyuk, Muzammil M. Hussain (CPD); The Spread of Mass Surveillance, 1995 to Present; In Their Blog at the Center for Political Studies (CPS), Institute for Social Research, University of Michigan; 2017-09-01.
Previously performed at the 2017 Annual Meeting of the American Political Science Association (APSA); the presentation, titled “Big Data Innovation Transfer and Governance in Emerging High Technology States” was a part of the session “The Role of Business in Information Technology and Politics” on Friday 2017-09-01.

tl;dr → an exercise in documentation; factoids are developed; a diversity is shown.
<quote>The observed cases in our study differ in scope and impact.</quote>

Original Sources


  • Aadhaar, a national ID program, India.
  • Social Credit System, China.


Categorical (arbitrary) Total Spend (USD) Spend/Individual (USD) Span (count) Coverage Universe Fun
nations worldwide $27.1B
(or more)
$7 4.138B 73% world population
stable autocracies,
authoritarian regimes
$10.967B $lower-$110 0.1B 81% their populations upper is 11X more
than “other regime type”
advanced democracies $8.909B $11 0.812B 74% their population
high-spending dictatorships and democracies,
developing and emerging democracies
$4.784B $1-2 2.875B 72% their population


AI and ‘Enormous Data’ Could Make Tech Giants Harder to Topple | Wired

AI and ‘Enormous Data’ Could Make Tech Giants Harder to Topple; ; In Wired; 2017-07-13.

tl;dr → <quote>such releases don’t usually offer much of value to potential competitors. </quote> They are promotional and self-serving.



  • TensorFlow
  • Common Visual Data Foundation
    • open image data sets
    • A “nonprofit”
    • Sponsors
      • Facebook
      • Microsoft
  • Other data sets
    • from YouTube, by Google
    • from Wikipedia, by Salesforce


  • Google
  • Microsoft
  • and others!
    • Salesforce
    • Uber
  • Manifold, a boutique
  •, a boutique


  • Luke de Oliveira
    • partner, Manifold
    • temp staff, visitor, Lawrence Berkeley National Lab
  • Abhinav Gupta, Carnegie Mellon University (CMU)
  • Rachel Thomas, cofounder,


Enormous Data
Are you kidding me? Do you even use computers?
incumbents’ usual data advantage
innovative and un-monopolistic by disruption
Appears in the 1st paragraph


The Wikitext Long Term Dependency Language Modeling Dataset; On Some Site

  • an announcement, but WHEN?


In Wired