Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapters
arXiv:2603.23780v1 Announce Type: new Abstract: Large Language Models (LLMs) have introduced new capabilities to recommender systems, enabling dynamic, context-aware, and conversational recommendations. However, LLM-based recommender systems inherit and may amplify social biases embedded in their pre-training data, especially when demographic cues are present. Existing fairness solutions either require extra parameters fine-tuning, or suffer from optimization instability. We propose a lightweight and scalable bias mitigation method that combines a kernelized Iterative Null-space Projection (INLP) with a gated Mixture-of-Experts (MoE) adapter. Our approach estimates a closed-form projection that removes single or multiple sensitive attributes from LLM representations with no additional trainable parameters. To preserve task utility, we introduce a two-level MoE adapter that selectively restores useful signals without reintroducing bias. Experiments on two public datasets show that our
arXiv:2603.23780v1 Announce Type: new Abstract: Large Language Models (LLMs) have introduced new capabilities to recommender systems, enabling dynamic, context-aware, and conversational recommendations. However, LLM-based recommender systems inherit and may amplify social biases embedded in their pre-training data, especially when demographic cues are present. Existing fairness solutions either require extra parameters fine-tuning, or suffer from optimization instability. We propose a lightweight and scalable bias mitigation method that combines a kernelized Iterative Null-space Projection (INLP) with a gated Mixture-of-Experts (MoE) adapter. Our approach estimates a closed-form projection that removes single or multiple sensitive attributes from LLM representations with no additional trainable parameters. To preserve task utility, we introduce a two-level MoE adapter that selectively restores useful signals without reintroducing bias. Experiments on two public datasets show that our method reduces attribute leakage across multiple protected variables while maintaining competitive recommendation accuracy.
Executive Summary
This article introduces a novel bias mitigation method for Large Language Model (LLM)-based recommender systems, combining kernelized Iterative Null-space Projection (INLP) with a gated Mixture-of-Experts (MoE) adapter. The proposed approach, termed Lightweight Fairness, estimates a closed-form projection to remove sensitive attributes from LLM representations without additional trainable parameters. A two-level MoE adapter is employed to selectively restore useful signals while preserving task utility. Experiments on public datasets demonstrate the efficacy of Lightweight Fairness in reducing attribute leakage across multiple protected variables while maintaining competitive recommendation accuracy. This contribution addresses a critical concern in LLM-based recommender systems, social bias amplification, and offers a promising solution for real-world applications.
Key Points
- ▸ Kernelized Iterative Null-space Projection (INLP) is combined with a gated Mixture-of-Experts (MoE) adapter for bias mitigation.
- ▸ The approach estimates a closed-form projection to remove sensitive attributes from LLM representations without additional trainable parameters.
- ▸ A two-level MoE adapter is employed to selectively restore useful signals while preserving task utility.
Merits
Strength in Addressing Social Bias
The proposed method addresses a critical concern in LLM-based recommender systems, social bias amplification, and offers a promising solution for real-world applications.
Scalability and Efficiency
Lightweight Fairness is a lightweight and scalable bias mitigation method that does not require extra parameters fine-tuning, making it a practical solution for large-scale applications.
Demerits
Limited Generalizability
The approach may not be widely generalizable to various LLM architectures and datasets, requiring further investigation and adaptation for specific use cases.
Dependence on Dataset Quality
The effectiveness of Lightweight Fairness heavily relies on the quality and representation of the training data, which may introduce additional challenges in real-world scenarios.
Expert Commentary
The article presents a well-structured and well-reasoned contribution to the field of bias mitigation in LLM-based recommender systems. The proposed Lightweight Fairness method offers a promising solution for addressing social bias amplification in these systems. However, the approach may require further investigation and adaptation for specific use cases, and its effectiveness heavily relies on the quality and representation of the training data. The article's implications for the development of fair and unbiased recommender systems are significant, and its contribution to the growing body of research on fairness in LLM-based recommender systems is noteworthy.
Recommendations
- ✓ Further investigation into the generalizability of Lightweight Fairness across various LLM architectures and datasets is recommended.
- ✓ The article highlights the need for policymakers and regulatory bodies to address the issue of social bias amplification in AI systems and to develop guidelines for the development and deployment of fair AI applications.
Sources
Original: arXiv - cs.LG
