Understanding and Mitigating Bias in MLLM-based Recommender Systems

Multimodal large language models (MLLMs) are increasingly adopted as backbones for recommender systems, giving rise to MLLM-based recommender systems (MLLM-RSs). However, bias issues in these systems remain largely underexplored, undermining stakeholders' trust and interests. Two key challenges to solve: How to (1) identify the source of residing bias and (2) mitigate bias without costly retraining. We propose TRACE (Tracing and Removing bias via cAusal Concept Effects)， a novel causally grounded framework to identify and mitigate bias in MLLM-RSs. First, it traces the propagation of bias through three connected levels: from the training sample level, to the modality-specific concept level that captures higher-level factors potentially driving bias, and finally to the recommendation outcome level that models how these concepts causally affect bias. We devise a causally-mediated chain influence to connect the three levels, attributing measured bias effects in recommendation outcomes to individual training samples. Second, the resulting chain influence directly guides three efficient bias mitigation operators: a removal operator suppresses high-influence bias-driving samples along identified bias directions; a corrective operator reinforces genuine preference signals from corrective pairs within the same directions; and a composed operator integrates both to balance bias removal and preference preservation during parameter updates. Experiments on three datasets and two MLLM backbones under different debiasing cases demonstrate that our proposed methods achieve a superior balance between recommendation fairness and accuracy.