1. The social life of information
I203 Social and Organizational Issues of Information

2. Course Stuff Assignment Schedule Set: see syllabus
Reading responses (3) spaced out between assignments
More info on final paper will be presented as part of lecture each week

3. Information Communication Systems and Technologies3

4. Other Information Communication Systems and Technologies…
..Paul Duguid and Geoff Nunberg’s course? 4

5. Thinking from a Social Science Perspective
Try to consider ideas about information and technology that would challenge your own assumptions.
Be very cautious about anything that supposedly relies on “common sense”.
*spoilier* warning: ‘common sense’ is not the same thing as ‘true’.
Try to think about technology in terms of processes and changes. 5

6. Datafication: A few highlights from “How Much Information 2003”
Print, film, magnetic, optical storage media: 5 exabytes of new info in 2002.
92% of new info stored on magnetic media.
Info flows through electronic channels (telephone, radio, etc) = 18 exabytes of new info in 2002; 98% of that from telephone alone.
Among P2P file sharing systems, only 7% actually share digital information goods.
Internet is fastest growing new medium of all time.
About 31 billion s sent daily (2002).

7. The Social “Periphery”
The social periphery are the communities, organizations and institutions that frame human activities.
Often seen as targets, not resources for design of information systems.
For example, “news” is not objective information from a source that is merely contaminated or framed from the people (i.e., journalists) who report it; “news” is the process from which information is interpreted and expressed to an audience.
Popeye and sweet pea example…

8. Atoms : trucks :: : 8

9. Information Solutions and Burdens
“Endism” in the information age
What exactly is “ending”, and why is that such a popular claim?
Examples:
Digital Music and the “Music Industry”
Paperless office?
Pencil? 9

10. Decentralization or Centralization?
“6-D” notion of the future…
Demassification….decentralization... denationalization…despacialization… disintermediation….disaggregation
…so what about Microsoft? Google? Yahoo? Others? 10

11. The inevitable flattening of hierarchy and organization?
Coase’s penguin…

12. “Decentralization” reconsidered: Open and Closed Systems
“open” systems
Open systems allow new members of a network to enter/exit through permeable boundaries.
E.g., online auctions, many public chatrooms
“closed” systems:
New members cannot easily enter a given network due to various restrictions (i.e., structural, legal, high cost)
Trade groups, private chatrooms. 12

13. Example: Wikipedia Openness creates risks for the online encyclopedia
Intentionally false information
Some information may lack “expert” knowledge
Deletion of information
Rings of hegemony?
Core group emerged to monitor and quickly fix problems.
Server/hardware management controlled by closed network of volunteers. 13

14. Wikipedia Example
‘open’ or ‘closed’ may best be thought of as a continuum rather than as two static conditions.
The open/closed nature of a system is may be related to competing dynamics of risk and growth.
Raises questions about how the structure of a given interaction situation affects the ‘open’ or ‘closed’ nature of a system. 14

15. Productivity Paradox
“administrative overhead, far from being curtailed by the introduction of office automation and subsequent information technologies, has increased steadily across a broad range of industries.”
-Paul Attewell 15

16. Information Systems as Agents
Solving routine information processing
Ordering a pizza…
Finding information on a specific website…
Suggestions based on preference tracking (product brokering)… 16

17. Personal Information Brokers
Social network-based information ordering
Inbox 2.0, Microsoft’s SNARF
Dynamic music DJ’s
iTunes, etc
But what about bias? (example of American Airlines system…or arguably many of todays search tools) 17

18. Human versus Bot “foibles”
Change our minds on the fly
Abandon “rules” when it might hinder progress or larger goals
Bots
Fairly blind to complex social trade-offs and competing goals
What is wrong with a price-maximizing and rational bot? 18

19. Obedience and tacit knowledge in information systems
Humans may have a specific goal, yet the achievement of this goal may be a product of many different (and seemingly irrational) actions.
How does one model such behavior in a bot or other information gathering/processing system– or should we even try? 19

20. A new problem?
Bots and other software tools echo machines and industrialization in earlier eras.
Doing mundane tasks for humans (printing press, assembly line machines)
Representing humans (voic , junk mail) 20

21. For Next Week: