Facebook Failed to Protect 30 Million Users From Having Their Data Harvested by Trump Campaign Affiliate | The Intercept

Facebook Failed to Protect 30 Million Users From Having Their Data Harvested by Trump Campaign Affiliate; Mattathias Schwartz; In The Intercept; 2017-03-30.

tl;dr → the tale

W3C Payment Request API is Being Implemented in All Major Browsers | ProgrammableWeb

W3C Payment Request API is Being Implemented in All Major Browsers; Janet Wagner; In ProgrammableWeb; 2017-09-20.

Original Sources

Mentions

Participants

  • Chrome,
  • Edge,
  • Firefox,
  • WebKit.
  • Facebook
    • Facebook Messenger Extensions SDK
  • Samsung
    • Samsung Internet for Android 5.

Quoted

For color, background & verisimilitude…

  • Ian Jacobs, Lead, Web Payments Working Group, W3C.
  • Lukasz Olejnik, expert
    • Dr. Lukasz Olejnik
    • site

Turns Out Algorithms Are Racist | New Republic

Turns Out Algorithms Are Racist; Navneet Alang; In The New Republic; 2017-08-31.
Teaser: Artificial intelligence is becoming a greater part of our daily lives, but the technologies can contain dangerous biases and assumptions—and we’re only beginning to understand the consequences.

tl;dr → Cites a Wired essay in the first ‘graph.  Hangs the tale off of that.
and → then s/Sexist/Racist/g; we saw what you did there.

Original Sources

Machines Taught by Photos Learn a Sexist View of Women;; In Wired; 2017-08-21.

Mentions

Referenced

Previously

In The New Republic

As Microsoft Joins Coalition for Better Ads, Blocking by Browsers Looks Set to Spread | Advertising Age

As Microsoft Joins Coalition for Better Ads, Blocking by Browsers Looks Set to Spread; ; In Advertising Age; 2017-09-20.

tl;dr → Microsoft has joined the Coalition for Better Ads.

Original Sources

Rik van der Kooi (Microsoft); Microsoft Joins The Coalition For Better Ads; In Their Blog; 2017-09.
Rik van der Kooi is corporate VP for search advertising, Microsoft.

Mentions

  • Microsoft
  • Coalition for Better Ads (CBA)
    • for Chrome
    • of Google
  • Edge
    • a browser
    • of Microsoft
  • <could><eventually>unilaterally block ads that coalition research editorial has deemed annoying.</eventually></could>
  • Google
  • Will call it “ad filtering” going forward
    <quote>The term “blocking” carries a lot of baggage.</quote>
  • <quote>Chrome browser will start “filtering” in “early” 2018.
  • Digital Content Next
    • a trade association
    • for online publishers
    • member, CBA
  • Adblock Plus
    • Eyeo
    • <quote>charges [large] companies fees to participate in its whitelisting program<quote>
    • The business model is extortion, attributed to Randall Rothenberg.
      The spox of Microsoft did not <quote>immediately respond to a request for comment on that point.</quote> [but did she later?]

Membership

  • Procter & Gamble
  • Unilever
  • WPP’s GroupM
  • Facebook
  • Thomson, of Reuters
  • The Washington Post
  • Interactive Advertising Bureau (IAB)
  • Association of National Advertisers (4As)
  • Digital Content Next, a trade association for online publishers and a coalition member itself.
  • <ahem>…and more!</ahem>

Quoted

For color, background &&amp verisimilitude…

  • A spox, a ‘droid, presented as a woman, Microsoft.
  • Rik van der Kooi, corporate VP for search advertising, Microsoft.
  • Satya Nadella, CEO, Microsoft.
  • Jason Kint, CEO, Digital Content Next.
  • Randall Rothenberg, CEO, Interactive Advertising Bureau (IAB).

Previously

In Advertising Age

Social Discounting Theory answers “How Much Is the Future Worth?” | Slate

How Much Is the Future Worth?; ; Series “Future of the Future,” in Slate;2017-09 (no specific date)

Mentions

  • Discount Rate
    time-value-of-valuablething;
    you know, like from undergraduate B-school.
  • Social Discounting Theory
  • Integrated Assessment Model (IAM)
  • Social Cost of Carbon (SCC)
  • Climate Policy Design

Referenced

  • pure time preference, definitional; at some educational site
  • three “500-year” storms
  • The Stern Review, referenced via their archives.
  • Some Other Report about The Stern Review, UK; referenced in Their Archives
  • Some book; in Google Books.
  • Some Article; Some Cub Reporter; In The Guardian; 2008-06-26.
  • Nordhaus? Stern?; Some Paper (Stern’s approach to discounting); Hosted at Yale University; 2008-05-03.
  • Nordhaus, Stern; Some Article; with the word “science”; Hosted at Yale University
  • Laurie Johnson; Some Paper; In Some Venue, Surely; 2012; paywall
  • Climate Cost Project
  • An Article; In Science News; WHEN?
  • Burke, Craxton, Kolstad, Onda; Some Paper (a “deep and subtle discussion of discount rates”); 2016.
    <quote>First, we discuss the social cost of carbon (SSC) and how it could be improved, including the consideration of catastrophes, nonmarket damages, impacts in developing countries, growth versus level effects, adaptation, and the use of discount rates. We then turn our attention to the integrated assessment models (IAMs) used in the computation of the SCC, arguing that, in addition to the need for incorporating the latest scientific understanding, we need to examine leading models’ consideration of uncertainty, the aggregation of heterogeneous agents, and technology options. Finally, we look at ways to improve climate policy design, in particular through the use of ex post analyses, insights from behavioral economics, the consideration of technology policy, and considerations specific to the developing world. With significant time and resources, we believe that progress can be made and many of these gaps filled.</quote>
  • canceling the entire project; Some Cub Reporter (SCR); In Politico; 2017-03.
  • Houston Cyptess; at Pro Publica.

Previously

In Slate

PicoRadio Supports Ad Hoc Ultra-Low Power Wireless Networking

Jan M. Rabaey, M. Josie Ammer, Julio L. da Silva Jr., Danny Patel, Shad Roundy (UCB); PicoRadio Supports Ad Hoc Ultra-Low Power Wireless Networking; In IEEE Computer; 2000; 8 pages.
Teaser: One of the most compelling challenges of the next decade is the “last- meter” problem—extending the expanding data network into end-user data-collection and monitoring devices. PicoRadio supports the assembly of an ad hoc wireless network of self-contained mesoscale, low-cost, low-energy sensor and monitor nodes.

tl;dr → early a eighteen years ago; nearly one human generation ago.

Mentions

  • Pico Radio
  • Berkeley Wireless Research Center (BWRC)
  • University of California Berkeley (UCB)

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].

Laws

  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.

What should you think about when using Facebook? | Boykis

Vicki Boykis; What should you think about when using Facebook?; In Her Blog, centrally hosted on GitHub; 2017-02-01

tl;dr → Facebook is bad.

Mentions

All the ways that Facebook is bad…

HP Brings Back Obnoxious DRM That Cripples Competing Printer Cartridges | Techdirt

Karl Bode; HP Brings Back Obnoxious DRM That Cripples Competing Printer Cartridges; In His Blog, entitled Techdirt; 2017-09-19.

Mentions

  • Dynamic Security

Quotes

<quote>Customers can head to the HP support website and download an alternate firmware without the Dynamic Security platform embedded (something that HP knows most users won’t do, and which places the onus for remedying HP’s bad behavior on the end user). Users then have to block HP’s automatic update functionality to prevent this firmware from being installed automatically (at the cost of useful updates).</quote>

Big Data Surveillance: The Case of Policing | Sarah Brayne

Abstract

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.

Conclusions

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.

Mentions

  • 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)
    program)

Argot

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

Quotes

<quote>Originally
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.

Promotions

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