Skip to main content

Machine Unlearning #1 (Classification)

You can’t conclude a discussion on Machine Learning without mentioning classification. Classification is a machine learning technique where the machine is trained to predict the label of the given input data.

Alright, let’s cut the jargon and get some real-world examples. Oranges and Bananas.

Let’s assume that we have a box of fruits that contain some oranges and some bananas. You are asked to pick one fruit at random and tell if it is an orange or a banana.

Pretty basic, right?

For us, it is straightforward. We would know the answer at first sight. But, how would a computer be able to tell the difference?

In classification, the machine would first be trained on some pre-labeled data. It would be shown an orange and we would tell it that the fruit is an orange. The machine would study the orange and remember its features - orange color and round shape. Then it would be shown a banana and the process is repeated. What are these features? A feature is anything that helps us uniquely label the data. In our example, the color, size, shape - all could be features.

Oranges are round in shape and orange in color. Bananas are elongated and yellow in color. When the computer is given a random fruit, it would check the features of the fruit. If the fruit is round in shape and has an orangish hue, it might label the fruit as an orange.

Now, since we have a basic idea of what classification is, let us find out how we apply similar concepts in our lives. Our lives are full of labels. We get labeled based on our faith, our place of birth, the language we speak, our political affiliations, or even our generic belief systems. Though such labeling has always been there, the rise in usage of the same has shot up with the advent of social media.

Social media is where people from diverse backgrounds converge. It is only natural that a difference in opinion would ensue. However, labeling (and trying to degrade) people who have engaged in a debate with you on social media platforms based on the afore-mentioned features has been a disturbing trend of late.

More examples!

Suppose that news channel ABC posted an update about a key announcement by the government. Under that post, your friend X wrote a comment questioning the logic behind the latest announcement. Unsurprisingly, many replies would spring up under this comment, most interested in shutting your friend down and assigning them a label rather than actually discussing the point raised. Now, the label to be assigned to your friend depends on some features, the most prominent one being their name.

If X has a name that resembles that of the majority community, they are most likely to be labeled as a “sickular liberal” (Yes! New words are being invented). On the other hand, if X is from a minority community, chances are that they would be straight up labeled an “anti-national”.
Limiting labeling to the social media universe seems unfair since the practice is religiously followed outside of the virtual world as well. ‘Outspoken’ and ‘arrogant’ are common labels used to describe children who have a different opinion to the elders of the house. 

A popular female actor, who would traditionally be labeled ‘item’ and ‘bomb’ by the misogynistic mob, would easily be labeled a ‘feminazi’ the moment she decides to display her mind on the screen instead of skin.

What exactly is wrong with labeling? Labeling, inherently, is not wrong. But when we use labels to counter a point raised by a person, we reduce them to just that word. The questions raised by the person are then seen as politically motivated ramblings of the group represented by the label. This hinders healthy discussions and promotes bias.

How cool would it have been if we all looked at the points asked objectively without doting on the person who asked the question? Would that be a reality ever? I don’t know. I am agnostic! 

Machine Unlearning is a series broken up into tiny, one-minute readable pieces to humor our ever-shortening attention span. Sharing the links to every single piece right below:


Popular posts from this blog

Movie Review : The Cabinet of Dr. Caligari

Title : The Cabinet of Dr. Caligari Language : Silent Movie Year : 1920 Director : Robert Wiene Genre : Horror IMDB Link Watch movie on YouTube Lead Role :   Friedrich Feher, Werner Krauss The movie is widely acknowledged as one of the landmark revolutionary offerings from the long gone era when movies did not speak. It may be technically incorrect to call a silent film German, nevertheless it was made in Germany during a time period when the European nation was in turmoils after the devastating World War I. The story begins with a young man by the name of Francis starts narrating the hardships faced by him and his fiancee (Jane) and the very peculiar, even horrifying doings of a strange man, Dr. Caligari. Dr. Caligari owns a stall at a nearby exhibition, and on display is a somnambulist Caeser, who allegedly has slept for 23 straight years! The doctor awakens him, and he answers questions asked by the spectators. To the horror of the locals, his prophecies comes true. Mean

Planet Perillamus

  Planet Perillamus An excited Ethoruthan broadcasted his findings to the Inter Universe Lifeform Detection Council. ‘I have discovered life on another planet.’ ‘Oh not again, Mx Ethoruthan!’, the Chairman of the council, Dan Maraman, shot back.‘ This is the eleventh time you are making such a claim over the last six months. How many missions have we launched to verify your claim - and have even one bore any result? These voyages are damn expensive, you know.’ ‘Please hear me out, Mx Maraman! This is not like the previous cases. I have proof.’ ‘What proof?’ Senior agent Thengaenthu was intrigued. ‘Do I have permission to present my thoughts to the council?’ Ethoruthan asked Maraman, Dan. ‘Yeah! You may.’ the Chairman relented warily. ‘Okay here is the interesting part. The life forms on this planet have devised something called movies - where some of them write unreal descriptions about unreal persons, and someone else would behave like those unreal persons. These behaviors would be re

This and that

You might have come across this joke that has been doing rounds for years now. Let me tell it to you nevertheless. It unfurls in a classroom, where the teacher is conducting an exam. The students are to write an essay on coconut trees. (What other topic to write on in a state named after coconut trees!) While all the kids went gaga over it, Veer was yet to write a word. Veer was not from the state named after coconut trees, and he knew scant about those. This is not to imply that his was a gone case. He decided to make up for his lack of knowledge in coconuts by leveraging his extensive knowledge about cows. This is how we wrote his essay: Coconut trees are good trees. We have a coconut tree in our backyard. My uncle ties our cow to the coconut tree in the afternoon. The cow gives us milk everyday. My uncle loves our cow very much... In a system that rewards on the basis of the number of words written rather than the sense it carries, Veer passed the test in flying colors. He later hel