By definition, supervised learning is generally used to classify data or make predictions, whereas unsupervised learning is generally used to understand relationships within datasets. Therefore, supervised learning is much more resource-intensive due to labelled data. Various examples of supervised learning have been given in the assigned reading, such as spam detection as part of an email firewall, distinguishing between conglomerate and non-profit novels, and spotify’s recommended songs model. These differences made me think of the notebook we did previously with the one we did today. In unsupervised learning, we do not have any training dataset which is the plus point for supervised learning and therefore, it is the best predictor.

Sinykin and Roland talks in: Against Conglomeration: Nonprofit Publishing and American Literature After 1908” about how ‘multiculturalism’ evolved in the world of literature. It was started by the government to include the diverse population that defined the new America; however, during the process of establishing the ‘multiculturalism’, things ended up being categorized in the form of specific titles and reputation given to the authors (African American/ Asian American/ Indian American) who had no specific goals to achieve such prejudiced and racist titles that created categories in the name of diversity. But is this really a multiculturalism? Aren’t we categorizing people according to their race and expecting them to create their work on their cultural basis? Non-profits did this because they had a gain of money, and it was the government who promoted this which got standardized due to the profit-gain. However, apart from all the downside, we can not deny that due to non-profits, chances were given to those who were considered outsiders (non-white people) in the field of literary.

We are experiencing a similar situation in the current period where all the non-profits are collecting data to improve society and create less discrimination. However, they are facing a lot of challenges in doing so.  For example, Machine learnings (ML) algorithms are generated to pick candidates for hiring. In order to make unbiased decision, the algorithm has to be taught to not gender/race discriminate the candidate. According to supervised learning process, these algorithms would need the data on gender and race to align with the unbiasedness. Therefore, in reality, it is very hard to remove the gender and race-specified data as they are required to fight against the discrimination. However, most of the time it is misused at this certain place. As Ben Schmidt states in his article “the most important rule for thinking about artificial intelligence is that it’s deleterious effects are most likely in places where decision makers are perfectly happy to let changes in algorithms drive changes in society. Racial discrimination is the most obvious field where this happens”. Therefore, this is the most opportunistic area for the Feminist scholars to work on. I have provided a similar argument in the Notebook as well.

In conclusion, we can say that supervised learning is a really good feature of machine learning if used properly; or else, it can create many societal issues such as discrimination and racialization by categorizing things in groups.

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