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PowerPoint Presentation - Lecture

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... illegal under FISA as well as unconstitutional under the First ... search engines make many things (sometimes surprisingly) public. agre on 'elaboration' ... – PowerPoint PPT presentation

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Title: PowerPoint Presentation - Lecture


1

surveillance fdm 20c introduction to digital
media lecture 11.05.2007
warren sack / film digital media department /
university of california, santa cruz
2
last time
  • non-linear media

3
outline
  • history of and surveillance today
  • review of the capture model
  • definition of privacy
  • private versus public
  • civil versus economic
  • capture
  • efficient connections versus resistances
  • on the virtue of inefficiencies
  • lessig on monitoring and search
  • example monitoring on the web
  • example search on the web
  • gandy on data mining

4
surveillance
  • close watch kept over someone or something
  • Etymology French, from surveiller to watch over,
    from sur- veiller to watch, from Latin
    vigilare, from vigil watchful

5
panopticon (1791)
6
panopticon (1791)
7
claude-nicolas ledouxs salt plant at
arc-et-senans (1779)
8
salt plant at arc-et-senans (1779)
9
surveillance as a dream of the 18th enlightenment
  • Michel Foucault I would say that Bentham was
    the complement of Rousseau. What in fact was the
    Rousseauist dream that motivated many of the
    revolutionaries? It was the dream of a
    transparent society, visible and legible in each
    of its parts, the dream of there no longer
    existing any zones of darkness, zones established
    by the privledges of royal power or the
    prerogatives of some corporation.
  • the eye of power, a conversation with jean-pierre
    barou and michelle perrot

10
but...
  • They that can give up essential liberty to
    obtain a little temporary safety deserve neither
    liberty nor safety.
  • Benjamin Franklin, 1759 Historical Review of
    Pennsylvania

11
surveillance today
  • some artists and art groups concerned with
    surveillance
  • see the zkm show, ctrl space, 2001, curated by
    thomas y. levin
  • surveillance camera players
  • http//www.notbored.org/the-scp.html
  • institute for applied autonomy
  • http//www.appliedautonomy.com/isee/info.htm
  • julia scher
  • steve mann
  • http//www.eyetap.org/wearcam/shootingback/

12
technologies of surveillance
  • example viisage superbowl XXXV
  • the company www.viisage.com
  • the technology eigenfaces
  • white.media.mit.edu/vismod/demos/facerec/basic.htm
    l

13
from surveillance to dataveillance
  • dataveillance/spying
  • carnavor
  • echelon
  • total information awareness agency
  • now the terrorism information awareness project
  • name change as of may 21, 2003 to mollify
    congress worries about intrusion of the privacy
    of u.s. citizens
  • headed by convicted felon (former admiral) john
    poindexter
  • http//www.darpa.mil/darpatech2002/presentations/i
    ao_pdf/slides/poindexteriao.pdf
  • officially ended in september 2003, but see
    electronic frontier foundations update
    http//www.eff.org/Privacy/TIA/

14
warrantless wiretaps
  • Soon after the September 11, 2001 attacks U.S.
    President George W. Bush issued an executive
    order that authorized the National Security
    Agency (NSA) to conduct surveillance of certain
    telephone calls without obtaining a warrant from
    the Foreign Intelligence Surveillance Court
    (FISC) as stipulated by FISA.
  • In the case ACLU v. NSA, Detroit District Court
    judge Anna Diggs Taylor ruled on August 17, 2006
    that the program is illegal under FISA as well as
    unconstitutional under the First and Fourth
    Amendments of the United States Constitution. Her
    decision is stayed pending appeal. Wikipedia

15
patriot act and post 9/11
  • aclus analysis
  • see http//www.aclu.org/SafeandFree/SafeandFree.cf
    m?ID11813c207
  • new powers of surveillance, search and seizure
  • threat to the first, fourth, fifth, sixth, eighth
    and fourteenth amendments of the U.S. Constitution

16
surveillance model versus capture model
l
  • surveillance model is built upon visual
    metaphors and derives from historical experiences
    of secret police surveillance
  • capture model is built upon linguistic metaphors
    and takes as its prototype the deliberate
    reorganization of industrial work activities to
    allow computers to track them the work
    activities in real time
  • agre, p. 740

17
capture (in comparison with surveillance)
  • linguistic metaphors (e.g., grammars of action)
  • instrumentation and reorganization of existing
    activities
  • captured activity is assembled from standardized
    parts from an institutional setting
  • decentralized and hetrogeneous organization
  • the driving aims are not necessarily political,
    but philosophical/market driven

18
taylorism, fordism and grammars of action
ford assembly line circa 1925
19
privacy a definition
  • 1.
  • a. the quality or state of being apart from
    company or observation
  • b. SECLUSION freedom from unauthorized intrusion
    ltone's right to privacygt
  • 2. archaic a place of seclusion
  • source Merriam Webster

20
privacy a culturally specific definition
  • Does the U.S. Bill of Rights define an
    individuals right to privacy?
  • Not explicitly, but...
  • inferrably e.g., Amendment IV The right of the
    people to be secure in their persons, houses,
    papers, and effects, against unreasonable
    searches and seizures, shall not be violated, and
    no Warrants shall issue, but upon probable cause,
    supported by Oath or affirmation, and
    particularly describing the place to be searched,
    and the persons or things to be seized.
  • implicitly e.g., Amendment IX The enumeration
    in the Constitution, of certain rights, shall not
    be construed to deny or disparage others retained
    by the people.

21
whats missing from this picture?
private
public
22
what are the connections between the public and
the private?
  • private public
    state

  • social civil society
    economic sphere
  • see writings by hegel, arendt, gramsci, etc.
  • e.g., hegel civil society as the domain of
    rights and freedoms guaranteed by the state
  • gramsci on the disctinction between civil society
    and economic sphere

23
resistances between private and public
  • private
    public
  • what divides the private from the public?
  • what reduces the efficiency of the connections
    between private and public?

24
lessig on the merits of inefficiency
  • I am arguing that a kind of inefficiency should
    be built into these emerging technologies an
    inefficiency that makes it harder for these
    technologies to be misused. And of course it is
    hard to argue that we ought to build in features
    of the architecture of cyberspace that will make
    it more difficult for government to do its work.
    It is hard to argue that less is more.
  • Lessig, p. 19

25
lessig on inefficiency (continued)
  • But though hard, this is not an argument unknown
    in the history of constitutional democracies.
    Indeed, it is the core of much of the design of
    many of the most successful constitutional
    democracies that we build into such
    constitutions structures of restraint, that will
    check, and limit the efficiency of government, to
    protect against the tyranny of government.
  • Lessig, p. 19

26
gandy on the merits of inefficiency
  • ...data mining systems are designed to facilitate
    the identification and classification of
    individuals into distinct groups or segments.
    From the perspective of the commercial firm, and
    perhaps for the industry as a whole, we can
    understand the use of data mining as a
    discriminatory technology in the rational pursuit
    of profits. However, as a society organized under
    different principles, we have come to the
    conclusion that even relatively efficient
    techniques should be banned or limited because of
    what we have identified as unacceptable social
    consequences
  • Gandy, pp. 11-12

27
digital media versus computer science
  • digital media studies some architectures (e..g.,
    democratic ones) are best designed to be
    inefficient
  • computer science efficiency is almost always
    considered to be a virtue efficient
    architectures are usually good architectures

28
lessig on architecture
  • however, by architecture lessig means, more or
    less, what computer scientists mean when they say
    architecture configuration/assemblages of
    hardware and software

29
lessig on code and architecture
  • The code of cyberspace -- whether the Internet,
    or net within the Internet -- the code of
    cyberspace defines that space. It constitutes
    that space. And as with any constitution, it
    builds within itself a set of values, and
    possibilities, that governs life there ... I've
    been selling the idea that we should assure that
    our values get architected into this code. That
    if this code reflects values, then we should
    identify the values that come from our tradition
    -- privacy, free speech, anonymity, access -- and
    insist that this code embrace them if it is to
    embrace values at all. Or more specifically
    still I've been arguing that we should look to
    the structure of our constitutional tradition,
    and extract from it the values that are
    constituted by it, and carry these values into
    the world of the Internet's governance -- whether
    the governance is through code, or the governance
    is through people.
  • Open Code and Open Societies Values of Internet
    Governance Larry Lessig (1999)

30
lessig on architecture of privacy
  • Life where less is monitored is a life more
    private and life where less can (legally
    perhaps) be searched is also a life more private.
    Thus understanding the technologies of these two
    different ideas understanding, as it were,
    their architecture is to understand something
    of the privacy that any particular context makes
    possible.
  • Lessig, p. 1

31
architectures of privacy
  • from doors, windows and fences
  • to wires, networks, wireless networks, databases
    and search engines

32
lessig
  • monitoring
  • search

33
monitoring on the web
  • what does your web browser reveal about you?
  • standard HTTP headers
  • From Users email address
  • User-Agent Users browser software
  • Referer Page user cam from by following a link
  • Authorization User name and password
  • Client-IP Client IP address
  • Cookie Server-generated ID label

34
cookies
  • cookies are information that a web server stores
    on the machine running a web browser
  • try clearing all of the cookies in your web
    browser and the visit the www.nytimes.com site

35
encyption
  • symmetric key encryption
  • public key encryption

36
search/elaboration/data mining
  • what lessig calls search,
  • what agre calls elaboration, and
  • what gandy discusses as data mining
  • are converging concerns about the production of a
    permanent, inspectible record of ones non-public
    life and thus a shrinking in size and kind of
    ones private life

37
searching on the web
  • search engines make many things (sometimes
    surprisingly) public

38
agre on elaboration
  • The captured activity records, which are in
    economic terms among the products of the
    reorganized activity, can now be stored,
    inspected, audited, merged with other records,
    subjected to statistical analysis, ... and so
    forth.
  • p. 747

39
data mining is one form of elaboration
  • gandy (p. 4) on data mining
  • ...data mining is an applied statistical
    technique. The goal of any datamining exercise is
    the extraction of meaningful intelligence, or
    knowledge from the patterns that emerge within a
    database after it has been cleaned, sorted and
    processed....

40
goals of data mining
  • In general, data mining efforts are directed
    toward the generation of rules for the
    classification of objects. These objects might be
    people who are assigned to particular classes or
    categories, such as that group of folks who tend
    to make impulse buys from those displays near the
    check out counters at the supermarket. The
    generation of rules may also be focused on
    discriminating, or distinguishing between two
    related, but meaningfully distinct classes, such
    as those folks who nearly always use coupons,
    and those who tend to pay full price. Gandy, p.
    5.

41
types of data mining
  • descriptive compute a relatively concise,
    description of a large data set
  • predictive predict unknown values for a variable
    for one or more known variables
  • e.g., will this person likely pay their bills on
    time?

42
data mining tasks
  • regression
  • classification
  • clustering
  • inference of associative rules
  • inference of sequential patterns

43
data mining task
  • regression infer a function that relates a known
    variable to an unknown variable
  • e.g., advertising how much will sales increase
    for every extra 1000 spent on advertising?

44
data mining task
  • classification given a set of categories and a
    datum, put it into the correct category
  • e.g., direct-mail marketing given a persons zip
    code, age, income, etc. predict if they are
    likely to buy a new product

45
data mining task
  • clustering given a data set divide it into
    groups
  • e.g., segmenting customers into markets given a
    set of statistics (e.g., age, income, zip code,
    buying habits) about a large number of consumers,
    divide them into markets e.g., yuppie, soccer
    mom, etc.

46
data mining task
  • inference of sequential patterns given a set of
    series, determine which things often occur before
    others
  • e.g., predicting a customers next purchase
    determine which products are bought in a series
    e.g., bookstore intro to spanish 1, intro to
    spanish 2, don quixote e.g., nursery grass
    seed, fertilizer, lawn mower

47
data mining task
  • inference of associative rules given a set of
    sets, determine which subsets commonly occur
    together
  • e.g., supermarket layout given a database of
    items customers have bought at the same time,
    determine which items should adjacent in the
    store e.g., if diapers and milk are often bought
    with beer, then place the beer next to the milk.
  • e.g., amazon.coms people who bought this book
    also bought...
  • Amazons feature is an example of a recommender
    system or a collaborative filter

48
data mining applications
  • data mining is used for
  • market research and other commercial purposes
  • science (e.g., genomics research)
  • intelligence gathering (e.g., identification of
    suspects by homeland security)
  • might data mining be used for the purposes of
    less powerful citizens? e.g.,
  • news analysis (cf, the function of FAIR)
  • government watch dog operations (cf., Amnesty
    International)

49
technologies and architectures of privacy
  • technologies and architectures are important
    influences on the production and change of
    private and public space
  • but, they do not independently determine what is
    public and what is private (to think they do is
    called technological determinism)
  • we need to understand not just the machines, but
    also the people mediated by these technologies
    we need to understand the whole as a machination,
    a heterogeneous network of people and machines
    thus lessigs mention (in addition to
    architecture) of laws, norms, and the market

50
architectures and inefficiencies
  • sometimes inefficient architectures, inefficient
    technologies are good technologies because they
    allow for or facilitate resistance by the less
    powerful in the face of powerful individuals,
    corporations and governments

51
next time
  • social networks / social software
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