Racism Spectrum
Ye Wang’s Scandal Project
Be like a panda. It’s white and black and asian.
I got interested in racism accidentally after coming to the US. But the word “racism” itself is very
hard to define. At the mean time people have racial discrimination unconsciously and they don’t
think themselves racists. In this situation, I decided to abandon the concept “racist” and conduct
an experiment to categorize people by their racial related behaviors.
I decided to make it a game to collect the behavioral datas, based on the belief that people’s
virtual avatar tend to reveal their inner egos. In a virtual world where everyone acts anonymously,
people’s dangerous ideas are augmented. This is why I need a game full of violence to dig
human’s dangerous wildness.
At first, I was thinking about a Grand Theft Auto like realistic sandbox but later I switched to sci­fi
with space aliens. My reason was, the outset of racism is the tiny racial differences leading to
stereotypes, and space races (or maybe it’s better to use the word species?) can give game
designers more freedom to create reasonable stereotypes. Just imagine what a spark happens
when virtual stereotypes and real racial stereotypes encounter...
My prototype game is Mass Effect and happens in the space station Omega and in a time before
human become a part of the Galaxy Civilization.
The game features many alien races (no human existence in the game) with different interesting
characteristics. After signing up, each player will be assigned with a character, of fixed age,
gender and RACE. The player is told that the in­game race is totally random, which is actually
not. In fact, the in­game race is the representation of a certain human race’s common
acknowledged stereotype.
(The following is what happens in my fantasy)
At the beginning the game will be played just like other online games. But gradually, people begin
to discover the secret of the game. So different things happen in virtual and real world.
In the real world, the game becomes the scandal. Everyone’s saying BioXare has made the
biggest racist game. Anti­racists are urging the game to be closed.
In the game world, players’ attitude toward other races begin to change. Racial conflicts are
emerging and deteriorating.
In two weeks time, the game is closed.
(fantasy stops here..)
The closure of the game begins the second part of the project­­data analysis.
All malicious acts fall into one of the following seven groups:
Violence, Verbal Abuse, Hostile Attitude, Indifference, Partiality, Supremacism, Brainwash
Every players is rated in these 7 sections from 0 to 10, based on the difference in their behavior
before and after discovering the secret of the game.
Since the game is not really made, I generated a set of hypothetical data of 5000 players using
Gaussian distribution. The following table is a part of my data:
This is quite difficult to read so I used the table generated many graphs in Processing and this is
my favorite one, peaks from left to right presents the 7 behavior categories I mentioned:
This one is like cocoons armed with swords, the two sides of closet racism. On one side, racists try to hide
themselves in cocoons, and extremist is already out of the closets. On the other side, racism is like swords, bled
others.
The next step is visualizing the data in 3d and clustering them in corresponding groups. For this
step, I used Octave, a Matlab like mathematical programming tool. Since the current data is 7d, I
have to lower the dimension to 3d first, using an algorithm called “Principal Component
Analysis”. After this, K­means Algorithm is used to separate the data into k groups (the value of k
is determined by balancing the accuracy and simplicity of clustering). This is what I got after
clustering:
Each cluster is labeled with a specific color. But what does this dandelion like stuff mean?
Now I can go back to my original 7d data. Every row of the data is assigned to a certain group.
What if I draw a curve for each row with the color assigned? The following graph is the answer.
Though lines are entangled with each other, some patterns could still be found. For examples,
yellow samples are more easily to be found in high political racism areas; green samples looks
like the average of the population; Purple ones have little to do with politics but show more violent
tendency; Blue ones seems to be the more mild players in the game world.
This is my first project involving machine learning and data visualizing which are two areas that
I’m quite interested in. But obviously I need to do more work with the clustering stuff. The final
result is not clear enough. For the game design part, the concept behind this is interesting and
radical enough, but to make the game out, the scale and vision need to be smaller. I may design
a more feasible game based on the concept of this project in the near future.
Inspirations:
Machine Learning Open Course by Stanford University
Turing Normalizing Machine
The Racismomaton
How I Learned to stop worrying and love discussing race by Jay Smooth
racism.org