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86 L. Valentino Bryant not intended to solely serve as a public information resource in the way that, say, a library might” (Noble 2019). Noble and her ground-breaking research has paved the way for a larger conversation from many angles which have brought inequities to light in burgening technologies. As a for-profit company, YouTube is not in a position to be an impartial third party, and indeed are acting in exactly the opposite of that role, pushing and prodding its viewers indiscriminately towards its advertisements. This manipulative system is not the root of the problem, but rather the directive that the algorithm had been told to aim for, resulting in an unintended consequence on its own. Youtube’s video recommendation system may be promoting racist viewpoints which is distorting the overall perception of content on YouTube as a whole, a dangerous misunderstanding since the platform has taken on the responsibility of providing not only amusement and entertainment for the masses, but informing and educating them as well. The measure of success for the YouTube algorithm is convincing the user to watch an additional video after the end of the first video has finished. The default behavior of the YouTube player is to immediately play the suggested video, an issue in itself with consent. The algorithm improves through machine learning which means every time it has a successful interaction, and a user allows one of the suggested videos to be played, the algorithm learns that there is a relationship between the video watched and the video suggested. Exactly how the algorithm works is a bit of a black box, some of its internal logic is opaque even to its engineers. The algorithm’s learned behavior, a process that takes place without human intervention, is internal. Google did publish a white paper in 2016 that reveals the engineering intent behind the design. The formula incorporates every moment that a video is watched as a positive number while videos that were not clicked end up as negative numbers, failures adding to the negative watch time (Covington, Adams, & Sargin, 2016). A user’s demographics which we see in Figure 1 as “example age” and “gender” is mentioned as a factor along with “items in a user’s history” which is supposed to accurately determine what kind of videos the person might want to watch next (Covington, Adams, & Sargin, 2016). The other factor mentioned is “search query tokens” which would imply that the keywords a user types into a search box would follow them around and inspire future recommendations (Covington, Adams, & Sargin, 2016). Figure 1. The YouTube algorithm is designed to use “example age” and other demographic information to anticipate what the user may want to watch next. Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for YouTube recom- mendations. RecSys ‘16 Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191-198. https://doi. org/10.1145/2959100.2959190PDF Image | YouTube Algorithm and the Alt-Right Filter Bubble
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