Thanks for joining me for another edition of the SerenityThroughSweat blog. While continuing my linguistics research I seem to have taken a fork in the road to information theory.
Sometimes you follow these paths to dead ends. But sometimes, the path leads to somewhere interesting even if it isn’t exactly where you thought you were heading, or needed to go in the first place.
Information theory was pioneered in the 1940’s and 50’s by Claude Shannon. We talked about him a little bit in the post on noise.
One of the ideas that helped kickstart Shannon’s theory, was that of the mathematician and logician George Boole.
George Boole in the laws of thought, explains the way that any question of logic can be turned into math. This is done with conditional statements AND, OR, NOT, and IF, along with an evaluation of if the statement is true 1, or false 0.
Imagine you want to find out how many people in your city are blonde women. The characteristic blonde can be represented by x and female by y. The statements will either be true 1, or false 0. AND would be represented by multiplication •, OR by addition +.
Each data point (person) can then be evaluated by the equations which can be translated easily back and forth between math and plain English.
1•1 = 1 blonde and female. 1•0=0 blonde and male. If you decide you are only concerned with how many women there are, 1+1=1 for the group of blonde women and 0+1=1 for the group of non blonde women.
This foundation laid by Boole in the 19th century set the stage for Shannon and other inventors to build our modern computing era. Boolian algebra would work with electrical circuits laid out either in parallel or in a series to evaluate the data.
Binary implies and either/or, true/false, 1 or 0. When setting code to evaluate these statements or questions, computation can be accomplished at lightning speeds.
This is why definitions are so important. As more and more of our world is driven by this binary code, true or false, statements can only be properly evaluated if we have agreed on the definitions.
This is a blessing for our modern information age. Tasks that would require huge amounts of human time and energy, and would be very error prone, can now be automated.
2+2=4. Is the picture of a stop sign. Are the letters in This scramble grstl. These can all be assigned yes or no values. True or false. And they are very simple examples. But as we move away from simple examples and in to more complex questions, the binary coding becomes more challenging.
Writing code to evaluate human defined terms is where I want to focus. The past few years has seen a rise in social media platforms restricting posts in one way or another.
Sometimes this is done by removing the posts entirely. Sometimes it is done by flagging the post, putting some sort of warning, or label, or explanation on it. Sometimes it is done by adjusting the post’s visibility.
Most of these restrictions are performed at least initially by a computer. A computer operating in binary. The post is true or false. It contains misinformation or it doesn’t. It contains banned content or it doesn’t.
This is not a blog post about censorship, those platforms policies, or one specific position over another. It is about the process. The mechanisms behind evaluating posted content.
If these posts are being flagged initially by an algorithm. That algorithm has to be programmed to observe certain characteristics or definitions.
As we saw from the onset, computers are faster and less error prone than humans at binary logic. When it comes to subjective rationalization, not so much.
If misinformation, or objectionable content, or hate speech is clearly defined, and we all agree on the definitions, then a binary logic calculation is magically fast and efficient.
However, if we go all the way back to 1964, to the court case Jacobellis V. Ohio which ultimately ended up in the supreme court, we see the root of the problem.
A movie theater was being sued for showing a movie with a sex scene. As the court case moved it’s way up the legal system to higher and higher courts, each court was unable to successfully define obscenity and pornography.
The problem is summed up well by justice Stewart in the popular legal quote “I shall not today attempt further to define the kinds of material I understand to be embraced within that shorthand description; and perhaps I could never succeed in intelligibly doing so. But I know it when I see it, and the motion picture involved in this case is not that.”
If humans “perhaps can never succeed in intelligibly defining” such terms, how can we expect a computer code, written by humans to do so?
Yet this is to a large extent the situation we find ourself in. Whoever controls the definition, and writes the code, establishes the binary. What is tru and what is false.
I have said it before, and I will say it again, words are important. The way we collectively define them is important. Participating in conversations about those definitions is important and everyone has the right to a voice in that conversation.
Thanks for joining me, stay safe and stay sweaty my friends.
