Balanced Explanations in Recommender Systems

Recommender systems have become essential in aiding users' decision-making processes, yet ensuring users understand the rationale behind recommendations remains a challenge. Balancing the presentation of both positive and negative aspects of recommendations is important to foster user trust and satisfaction. In this PhD thesis, I want to investigate the balance between supporting recommendations and alerting users to potential risks. While prior research has broadly examined persuasive and informative explanations, my focus is on exploring methodologies to effectively caution users about associated risks. By prioritizing the warning aspect, the aim is to heighten user awareness of biases and potential harms, ultimately enhancing transparency and trust in the recommendation process.