Analysing recommended content provided by YouTube and users preferences : A Case Study at Dalarna University in Sweden

The application YouTube released in 2005 is today known as the most prominent platform on the world wide web for sharing, creating, and discovering video content(s). By utilizing technology trends such as Machine learning, YouTube can take advantage of algorithms such as deep neural networks to entice the users with a massive amount of personalized recommended content(s)t to watch based on the user's data. The algorithm in the YouTube recommendation system collects the users' personalized data such as location, watch history and search history to recommend video content. The YouTube recommendation system gives personalized recommended content(s) to a YouTube user with the collected data. The user may deem the recommended content as ethically wrong. Therefore, bring up meaningful discussions such as a YouTube consumer's sentiment towards those contents they deem unethical. This research study examines what ethical challenges exist in the YouTube recommendation system and how those challenges affect the YouTube consumers trust in the recommendation system. • The research questions that were examined in this study are: What kind of ethical challenges does the YouTube recommendation system have? • What kind of impact do those ethical challenges have on the user’s trust in the YouTube recommendation system? The respondents consisted of students at Dalarna University. This research was conducted with semistructured interviews and a questionnaire that consisted of 11 questionnaires. By examining the results from the literature reviews, interviews, and questionnaire, the gathered results show that challenges such as inappropriate, misinformed content and privacy exist with the YouTube recommendation system. Numerous of our respondents felt like they did not trust the YouTube recommendation system with their personalized data, primarily because of receiving video content that is not relevant, which has made some of the respondent question what YouTube does with the influx of the individual data. Based on the result it can be concluded that there are many concerns related to ethical issues in the YouTube recommendation system. In essence, the respondents highlighted the significance of privacy when using a platform such as YouTube. The respondents desired more transparency with their data