Towards Decentralization of Social Media
This addresses the problem of user addiction and privacy exploitation in social media for the general public, but it is incremental as it builds on existing critiques without introducing a new paradigm.
The paper analyzes how Facebook's AI-driven recommendation system targets users based on their interactions, segregating them into communities to optimize ads and engagement. It proposes three methods to protect human vulnerabilities exploited by such systems.
Facebook uses Artificial Intelligence for targeting users with advertisements based on the events in which they engage like sharing, liking, making comments, posts by a friend, a group creation, etcetera. Each user interacts with these events in different ways, thus receiving different recommendations curated by Facebook's intelligent systems. Facebook segregates its users into chambers, fragmenting them into communities. The technology has completely changed the marketing domain. It is however caught in a race for our finite attention with a motive to make more and more money. Facebook is not a neutral product. It is programmed to get users addicted to it with a goal of gaining added information about the users and optimizing the recommendations provided to the users according to his or her preferences. This paper delineates how Facebook's recommendation system works and presents three methods to safeguard human vulnerabilities exploited by Facebook and other corporations.