CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System

Understand

This work takes a critical stance on previous studies concerning fairness evaluation in Large Language Model (LLM)-based recommender systems, which have primarily assessed consumer fairness by comparing recommendation lists generated with and without sensitive user attributes.

• Such approaches implicitly treat discrepancies in recommended items as biases, overlooking whether these changes might stem from genuine personalization aligned with the true preferences of users.
• Moreover, these earlier studies typically address single sensitive attributes in isolation, neglecting the complex interplay of intersectional identities.
• In response to these shortcomings, we introduce CFaiRLLM, an enhanced evaluation framework that not only incorporates true preference alignment but also rigorously examines intersectional fairness by considering overlapping sensitive attributes.