Large Language Model (LLM)-based recommendation systems provide more comprehensive recommendations than traditional systems by deeply analyzing content and user behavior. However, these systems often exhibit biases, favoring mainstream content while marginalizing non-traditional options due to skewed training data. This study investigates the intricate relationship between bias and LLM-based recommendation systems, with a focus on music, song, and book recommendations across diverse demographic and cultural groups. Through a comprehensive analysis conducted over different LLM-models, this paper evaluates the impact of bias on recommendation outcomes. Our findings highlight that biases are not only deeply embedded but also widely pervasive across these systems, emphasizing the substantial and widespread nature of the issue. Moreover, contextual information, such as socioeconomic status, further amplify these biases, demonstrating the complexity and depth of the challenges faced in creating fair recommendations across different groups.

Vertical Federated Learning (VFL) provides a secure, collaborative machine learning framework that allows multiple institutions, each holding different subsets of features, to jointly train models without sharing sensitive data. Despite its advantages, VFL requires a careful balance between multiple factors, such as explainability, privacy, and data security. Enhancing model interpretability often necessitates revealing more about the underlying data, which can compromise privacy. Conversely, strong privacy safeguards may obscure the model’s decision-making process, hindering explainability. In this paper, we explore this critical explainability-privacy trade-off and propose a novel framework designed to navigate this balance while ensuring robust utility and accuracy. We demonstrate the effectiveness of our framework using a real-world healthcare dataset, focusing on scenarios where both interpretability and privacy are paramount. The numerical experiments showcase how our approach maintains model accuracy while providing interpretable insights into predictions, all while preserving privacy at critical junctures. This work underscores the importance of addressing the explainability-privacy dichotomy in federated learning systems, offering a path toward building transparent, trustworthy AI models in sensitive domains such as healthcare.