aiqbench analysis
This research addresses a critical gap in parameter-efficient fine-tuning by developing fairness-aware LoRA strategies that function without requiring sensitive attribute labels alongside task data. The study demonstrates that orthogonality-based disentanglement and entropy maximization successfully balance utility and fairness, though it also highlights inherent trade-offs when optimizing multiple fairness criteria simultaneously. These findings offer a practical pathway for deploying efficient foundation models in privacy-constrained environments while maintaining equitable outcomes.
Fairness-Aware Low-Rank Representation Fine-Tuning
arXiv:2503.05684v2 Announce Type: replace Abstract: Pre-trained foundation models can be efficiently adapted for specific tasks using Low-Rank Adaptation (LoRA), but the fairness properties of these adapted classifiers remain underexplored. Existing fairness-aware fine-tuning methods assume that sensitive attribute labels are available alongside downstream task labels, which often fails in practice due to user consent limitations or privacy constraints. To address this gap, we investigate fairness-aware LoRA fine-tuning using separate datasets for downstream tasks and sensitive attributes. We introduce four fairness-aware LoRA strategies: sensitive unlearning, adversarial debiasing, orthogonality-based disentanglement, and entropy maximization. Through comprehensive experiments on standard algorithmic fairness datasets using an ImageNet pre-trained ViT-Base model, we evaluate these methods across multiple utility and fairness metrics. Our orthogonality-based disentanglement and entropy maximization approaches consistently outperform standard fine-tuning in both overall utility and fairness, while adversarial debiasing shows less consistent improvements and sensitive unlearning proves ineffective for classification tasks. However, fairness-aware methods underperform on certain metrics like subgroup-wise false-positive rate ratios, highlighting fundamental incompatibilities between fairness objectives. These findings demonstrate the potential of fairness-aware LoRA fine-tuning while revealing inherent challenges of simultaneously optimizing multiple fairness criteria in parameter-efficient adaptation.
Abstract:Pre-trained foundation models can be efficiently adapted for specific tasks using Low-Rank Adaptation (LoRA), but the fairness properties of these adapted classifiers remain underexplored. Existing fairness-aware fine-tuning methods assume that sensitive attribute labels are available alongside downstream task labels, which often fails in practice due to user consent limitations or privacy constraints. To address this gap, we investigate fairness-aware LoRA fine-tuning using separate datasets for downstream tasks and sensitive attributes. We introduce four fairness-aware LoRA strategies: sensitive unlearning, adversarial debiasing, orthogonality-based disentanglement, and entropy maximization. Through comprehensive experiments on standard algorithmic fairness datasets using an ImageNet pre-trained ViT-Base model, we evaluate these methods across multiple utility and fairness metrics. Our orthogonality-based disentanglement and entropy maximization approaches consistently outperform standard fine-tuning in both overall utility and fairness, while adversarial debiasing shows less consistent improvements and sensitive unlearning proves ineffective for classification tasks. However, fairness-aware methods underperform on certain metrics like subgroup-wise false-positive rate ratios, highlighting fundamental incompatibilities between fairness objectives. These findings demonstrate the potential of fairness-aware LoRA fine-tuning while revealing inherent challenges of simultaneously optimizing multiple fairness criteria in parameter-efficient adaptation.
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2503.05684 [cs.LG]
(or arXiv:2503.05684v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2503.05684 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 279 words
Related coverage
J++ Lens: Jacobian Filtering Enables More Faithful Workspace Lenses
LessWrong · Oct 8, 2026
Links #5: 2026/07
LessWrong · Jul 31, 2026
Small LLMs perform no-CoT two-hop reasoning on synthetic facts with real bridges
LessWrong · Oct 17, 2026
[AINews] High Taste Testers Ep. 1
Latent Space · Oct 17, 2026