Large Language Models (LLMs) have become ubiquitous in contemporary applications. They are trained on extensive collections of human writings, ranging from books, papers, and news articles to conversations on social media platforms. This comprehensive approach enables the development of sophisticated tools capable of emulating human interactions with remarkable fidelity. However, it is important to recognize that LLMs might inherit and perpetuate the biases inherent in human communications. This project represents a concerted effort to delve deeply into the multifaceted landscape of biases inherent in interactions with LLM agents. By examining various dimensions of biases, we aim to explore how these biases manifest within LLM-mediated interactions. Through this project, we would not only understand the complexities of bias within LLM interactions, but also explore the possibility to mitigate, neutralize, or rectify these biases. This will underline if LLMs are more inclined to change opinions than humans. This approach underscores our commitment to fostering fairness, equity, and inclusivity in the realm of LLM-driven communication and interaction, ultimately advancing the societal impact and ethical integrity of LLM technology.