Privacy in the Machine Learning: A Study on User Profiling and Targeted Advertising on LinkedIn

As the digital landscape changes, privacy concerns in machine learning applications need to be focused on. This research will investigate the privacy implications of the LinkedIn platform related to targeted advertising and user profiling. The main purpose of this research is to understand the algorithm used by LinkedIn to generate user profiles and the way they provide relevant advertising to the users. This research will use different methods, like interviews, user surveys, and data analysis. The first step is to look at the algorithms and processes used by the LinkedIn platform for collecting data and user profiling. To investigate what kind of data is collected and how they create user profiles, and to evaluate the level of control users have over their data. In the process of gathering information, surveys will be done on LinkedIn platform users to evaluate their level of concern and their awareness of the platform's advertising policies. A sample of users will be given interviews to get more qualitative feedback on the users' experiences. This research will check what types of advertising are given to the users that keep them engaged with the platform. The study will give a great picture of how users are taken advantage of by the platform to keep them on the platform, from their perspective on how there is a trade-off between their privacy on the platform and the content they get on the platform. In the end, this research is another catalyst in the huge conversation happening now on privacy concerns in machine learning by giving new suggestions on industry best practices on how to improve LinkedIn privacy policies. The findings will open discussion in different ways, from the platform itself to the legislators and users.