Robby the Racist Robot is a playful way for people to consider our mass collection of data and information, and how it can be used or misused depending on its context. The scanner gathers a skin tone sample and links the profile to a country of origin based on a pigment chromatic scale from the early 20th century. The system then loads and parses through X number of databases filled with up-to-date statistics for your country. For example, if 40% of the population is female, the system uses an algorithm and 40% of the time will determine you are female and 60% of the time you will be male. This is done with all of the statistics before speaking and printing the information. Obviously this will result in stats that are incorrect, and different, every time but are still based in fact.
Classes
Introduction to Computational Media – Professor Daniel Rozin
Description
As information, data and statistics continue to grow as consumables it is highly important for people to understand and pay attention to its use and misuse. This project asks for people to give the system a single source of information (skin tone) which is taken to create a fact-based stereotype (which will always be incorrect). Using algorithms for color proximity, CIA Factbook information, Von Luschan’s chromatic scale for skin pigmentation (originally developed in 1927), and various other methods, the user realizes although a single source, and viewpoint, of information may be relevant and can be used to distinguish between people; characteristics of people and their social environment are much more important.
Keywords
color detection, physical computing, statistics, processing, database, arduino, stereotyping, racism, visualization
Personal Statement
While in physical computing I was very intrigued with the idea of having physical “real world” devices connect with statistics and online information. I began studying the ability to combine both in an easy to use, yet informative and provocative way, as data can be represented in many contexts to mean very different things – although all information may be statistically “true” and credible.
Background
Study of pigmentation and chromatic scales (Von Luschan), Monte Carlo method to determine random-within-random numbers, Java based application support for multiple XLS documents, serial and parallel dot matrix printing capabilities, shell command scripting, video color tracking, array organization and sorting, color differentiation
Audience
Anyone with skin pigmentation. This project is intended to provoke interaction, fun, and consideration between people around the installation.
User Scenario
The user walks up to the device and places their hand into the scanner. When the hand is entered a light fills the area and the scan is taken. The information is then processed and filtered in the computer which speaks and prints the stereotype on an Okidata ML395 dot matrix printer for the user to take with them.
An example of the output may be:Country of origin: Niger
Sex: Male
Age: 54
Literacy: False
Oil Consumption: 4.5 mL
Expected lifespan: 58.2 years
Internet access: True
Occupation: Agriculture
etc
Implementation
An upside-down overhead projector case has been turned into the scanning device which includes a proximity sensor, camera and super bright LEDs for consistent lighting. An algorithm then averages the skin colors received and finds its closest relation.
A country of origin is chosen based on this color and information is pulled from ~50 databases of information from the CIA Factbook (among others).
The computer then uses a shell script command to speak the information (age, sex, country of origin, literacy, oil consumption, etc) and prints to an Okidata ML395 dot matrix printer, which is then available for the user to take with them.
Conclusions (stereotypes) will obviously come up different for each person and even a single person will receive very different results from each use.
Conclusion
Color scanners didn’t work, camera color tracking needed, broke lots of stuff (like the projector), hard to find dot matrix printer, hard to interface a 20 year old dot matrix printer with a new Macbook Pro and Arduino/Processing
Showings:
New York University: ITP Winter Show 2007
Press:
New York Magazine: Daily Intelligencer
Fulbright Academy / Finding America
G. Taylor McKnight (Chime.TV, Podbop)
References
Pigmentation scale (created in early 1920’s and concluded in 1940’s) : link
Monte Carlo method: http://en.wikipedia.org/wiki/Monte_Carlo_method
Okidata support team
Dan Shiffman
Syed Salahuddin
Personal Statement
While in physical computing I was very intrigued with the idea of having physical “real world” devices connect with statistics and online information. I began studying the ability to combine both in an easy to use, yet informative and provocative way, as data can be represented in many contexts to mean very different things – although all information may be statistically “true” and credible.
Background
Study of pigmentation and chromatic scales (Von Luschan), Monte Carlo method to determine random-within-random numbers, Java based application support for multiple XLS documents, serial and parallel dot matrix printing capabilities, shell command scripting, video color tracking, array organization and sorting, color differentiation
Audience
Anyone with skin pigmentation. This project is intended to provoke interaction, fun, and consideration between people around the installation.
User Scenario
The user walks up to the device and places their hand into the scanner. When the hand is entered a light fills the area and the scan is taken. The information is then processed and filtered in the computer which speaks and prints the stereotype on an Okidata ML395 dot matrix printer for the user to take with them.
An example of the output may be:Country of origin: Niger
Sex: Male
Age: 54
Literacy: False
Oil Consumption: 4.5 mL
Expected lifespan: 58.2 years
Internet access: True
Occupation: Agricultureetc
Implementation
An upside-down overhead projector case has been turned into the scanning device which includes a proximity sensor, camera and super bright LEDs for consistent lighting. An algorithm then averages the skin colors received and finds its closest relation.
A country of origin is chosen based on this color and information is pulled from ~50 databases of information from the CIA Factbook (among others).
The computer then uses a shell script command to speak the information (age, sex, country of origin, literacy, oil consumption, etc) and prints to an Okidata ML395 dot matrix printer, which is then available for the user to take with them.
Conclusions (stereotypes) will obviously come up different for each person and even a single person will receive very different results from each use.
Conclusion
Color scanners didn’t work, camera color tracking needed, broke lots of stuff (like the projector), hard to find dot matrix printer, hard to interface a 20 year old dot matrix printer with a new Macbook Pro and Arduino/Processing
Showings:
New York University: ITP Winter Show 2007
Press:
New York Magazine: Daily Intelligencer
Fulbright Academy / Finding America
G. Taylor McKnight (Chime.TV, Podbop)
References
Pigmentation scale (created in early 1920’s and concluded in 1940’s) : link
Monte Carlo method: http://en.wikipedia.org/wiki/Monte_Carlo_method
Okidata support team
Dan Shiffman
Syed Salahuddin