Currently, we live in a world shaped by AI and ML solutions. It is no longer surprising that, in our everyday online activities, various algorithms tailor the content we browse to our preferences. These are often incomplete or inaccurately inferred from our past interactions. This situation often leads to the problem of the information bubble, as AI-driven content personalization reinforces our existing views and limits access to diverse perspectives. The goal of this paper is to analyze existing mechanisms that address this problem. We approach the issue from multiple angles, considering algorithmic aspects as well as fairness and ethical concerns.