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