week 4. Reflection on Crowdsourcing: algorithms for minors

This week, I read Tyrrell and Shalavin’s (2022) paper about crowdsourcing. Crowdsourcing is based on the idea of 'collective intelligence.' It suggests that when a group of people works together, they can make smarter decisions than one genius. In this era with diverse perspectives, this sounds like a fair way to listen to everyone's thoughts. However, this paper also addresses the digital literacy-driven disparity. I began to see a different side: the risk of repeating dominant structures.

It is true that crowdsourcing is more open than traditional top-down methods. It gives a space for minority voices. But in reality, most platforms still favor the thoughts of the majority. When a system uses voting or comments to decide what is "best," the most popular opinions appear on the first screen, based on the innate algorithms. People then naturally assume that these top-ranked posts are the "right" ones. I am sure I am not the only one who feels tired of this. This is similar to informative online communities where existing members are cliquey, and +100 existing users reply to a newbie who has different opinions, which seems nice, but they end up making the newbie feel intimidated and overwhelmed by all the comments. This prevents new users from feeling free to join and share new thoughts, keeping the community controlled by the dominant ideas.

The majority opinion is not always the best one. To respect the voices of the minority, we need to change our current recommendation algorithms. Right now, most platforms just sort by "most likes." We should change this to make the space more balanced. For example, we could show "newest" posts as a basic algorithm, highlight "controversial" posts with many comments, or feature posts with very few comments that need more attention. Of course, netiquette is another important skill we need. (and I understand that there are some dweebs everywhere who enjoy derogating other people..)

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