FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Regularized Low-Rank Adaptation

Authors: Rohan Sukumaran, Aarash Feizi, Adriana Romero-Soriano, Golnoosh Farnadi
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: LoRA, ViTs, Fairness, Vision Models, Bias mitigation

Abstract:
Recent advances in parameter-efficient fine-tuning methods, such as Low Rank Adaptation (LoRA), have gained significant attention for their ability to efficiently adapt large foundational models to various downstream tasks. These methods are appreciated for achieving performance comparable to full fine-tuning on aggregate-level metrics, while significantly reducing computational costs. To systematically address fairness in LLMs previous studies fine-tune on fairness specific data using a larger LoRA rank than typically used. In this paper, we introduce FairLoRA, a novel fairness-specific regularizer for LoRA aimed at reducing performance disparities across data subgroups by minimizing per-class variance in loss. To the best of our knowledge, we are the first to introduce a fairness based finetuning through LoRA. Our results demonstrate that the need for higher ranks to mitigate bias is not universal; it depends on factors such as the pre-trained model, dataset, and task. More importantly, we systematically evaluate FairLoRA across various vision models, including ViT, DiNO, and CLIP, in scenarios involving distribution shifts. We further emphasize the necessity of using multiple fairness metrics to obtain a holistic assessment of fairness, rather than relying solely on the metric optimized during training.

Decision: reject
Reviewer scores: 3, 5, 6

Reviews and discussion:
<thread>
creation date:
1730348066640

thread_type:
official_review

<thread_object>
creator: reviewer_kvf3

<content>
summary:
This paper studies the fairness problem of the popular LoRA (Low-Rank Adaptation) technique. The authors propose a simple yet effective strategy for improving the fairness of LoRA, specifically by reducing the variance of per-group loss. The results are promising.

strengths:
- The topic of fairness in LoRA (Low-Rank Adaptation) is important and worthy of study.
- The proposed method of reducing group-wise variance is simple yet effective.
- The motivation behind the proposed method is clear.
- The method is evaluated on several visual foundation models, and the results are convincing.

weaknesses:
- How are the groups divided in page 4, line 212?
- The formulation in page 4, line 208 lacks a number.
- How are the groups divided in Section 5, "MEASURING FAIRNESS"? What is the difference between these groups and the groups mentioned in page 4, line 212?
- In my opinion, fairness means consistent results among different categories, such as cats and dogs. What is the relationship and difference between the category group and the groups you defined?
- The formulation of "Equalized Opportunity Difference (EOD)" is abstract. Please add a textual explanation.
-  This paper only studies the recognition problem. How does the proposed method generalize to more fine-grained tasks, such as segmentation? Category imbalance is a common issue in segmentation tasks.

questions:
- Explain the "Group" strategies.

- Using text to explain the formulations.

- How about adopt your approach on segmentation task.
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</thread>
<thread>
creation date:
1730463677993

thread_type:
official_review

<thread_object>
creator: reviewer_p37m

<content>
summary:
This paper introduces a novel fairness-based finetuning technique based on LoRA with a fairness-specific regularizer, studies multiple fairness metrics, and analyzes factors for bias mitigation. Experiments conducted on various vision models including ViT, DiNO, and CLIP support the discovery and analysis.

strengths:
1. This paper is the first to introduce a fairness-based fine-tuning technique using LoRA, as stated.

2. It presents multiple fairness metrics (Sec. 5) and conducts extensive experiments, supplementing previous fairness-centered evaluation metrics and contributing to future research.

3. The comprehensive experiments are visually presented with colorful figures, enhancing clarity and accessibility, especially for researchers who may not be familiar with ML fairness concepts.

4. Using these metrics, the paper also examines potential factors influencing FairLoRA’s fairness, such as LoRA ranks, distribution shifts from pretraining data, and task types, further contributing to the research community.

weaknesses:
1. The analysis on handling distribution shifts seems to be insufficient, as it includes only FID-based quantitative comparisons. Would it be possible to conduct additional experiments, such as t-SNE, to better demonstrate the effectiveness of distribution shift handling? For instance, showing the t-SNE point distributions before and after fine-tuning or comparing t-SNE point distributions of fine-tuned models with vanilla LoRA and FineLoRA could add valuable insights.

2. Several recent papers [1][2] also focus on LoRA fairness, discussing similar concepts, such as fairness metrics like EOD and LoRA ranks as fairness factors. Both papers were submitted in May 2024, more than three months before the ICLR submission deadline, yet they are not mentioned in the discussion. It might be helpful to clarify the distinctions in motivation, methodology, experiments, and findings between Submission 11853 and these related works. For instance, [1] notes that "lower ranks may retain the bias of the original model." Does this finding complement or challenge your conclusions? Since [1][2] evaluate LoRA fairness in LLMs rather than vision-language models like CLIP, could extending your experiments to LLMs provide additional insights?

3. In the Geode dataset, FairLoRA’s "Eod Max" value is significantly lower than that of the other methods (Fig. 4, 7). Are there additional comparisons or analyses to further investigate this? Providing a detailed analysis of why FairLoRA performs exceptionally well on this metric for this dataset, as well as exploring whether this trend holds across different model architectures or LoRA ranks, could strengthen the findings.

[1]	Das S, Romanelli M, Tran C, et al. Low-rank finetuning for LLMs: A fairness perspective[J]. arXiv preprint arXiv:2405.18572, 2024.

[2]	Ding Z, Liu K Z, Peetathawatchai P, et al. On Fairness of Low-Rank Adaptation of Large Models[J]. arXiv preprint arXiv:2405.17512, 2024.

questions:
1. If possible, it would be beneficial to include further comparisons and analyses, particularly visualizations, discussions of concurrent papers, and detailed analysis of the EOD in the Geode datasets, as noted in the Weaknesses section.

2. As I am not an expert in ML fairness, I’m curious about how you divide the datasets into different groups. Are the groups derived from inherent divisions in the dataset, random divisions, or based on the original VLM’s performance on each sample during inference? Could different group division strategies affect the performance and conclusions? It would be helpful if you could clarify the exact process for group division in each dataset and discuss how alternative division strategies might influence your results and conclusions about fairness.

3. In figure 5, why a LoRA Rank of 32 experiences a sudden drop in Accuracy and Min F1, compared with 16 and 64? Similarly, in Figure 9 there is a sudden drop of performance when LoRA rank is set to 16, compared with neighbored rank values. You may further analyze that if this pattern is consistent across different models or datasets, and what implications this might have for choosing optimal LoRA ranks in fairness-aware fine-tuning.
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</thread>
<thread>
creation date:
1730619655926

thread_type:
official_review

<thread_object>
creator: reviewer_42sa

<content>
summary:
This paper proposes to achieve Lora fairness by reducing the variance of per group loss at a mini-batch level, which can reduce the performance disparities of the Lora across all classes. Experiments on Aircrafts with 100 classes and Waterbirds with 2 classes show that the proposed method is somewhat effective.

strengths:
1. The investigation of fairness on Lora is interesting.

weaknesses:
1. This paper is difficult to read. 
- Figures: Fig.1, and 2,5 are not well illustrated, the font is too small to read clearly.  Also, the low variance between losses does not necessarily lead to good performance/fairness.
- Writing: The story is not clearly or well organized. Fairness is not well defined in the classification tasks (which is also very limited) involved in this paper. The fairness can be defined between classes on a specific task, or between different metrics, or between different tasks, or even on different domains, which is not even mentioned in this paper. The distinctions/relationships between fairness, good performance, and good generalization ability are not discussed.
2. Poor experiments.
- Experiments on two datasets are not convincing enough, and the dataset Waterbirds only has two classes, which is not a good choice to demonstrate fairness.
- Fairness may be largely affected by the long-tail distribution of the training data, and there are no experiments. Also, there should be some comparisons between strategies for long-tail problems and the proposed method.
- Experiments only show min and avg metrics, without the best metrics. It is doubted that the best group may be degenerated by the proposed method.
3. Limited novelty.
- Minimizing the group loss variance is straightforward and seems effective, but there should be more explanations, especially on why the results do not degenerate.

questions:
see weaknesses.
</content>
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</thread>
<thread>
creation date:
1733169807801

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Combined response to all reviewers highlighting all the changes

comment:
We sincerely thank the reviewers for their constructive feedback. Below, we address the main comments, grouped by themes, and provide clarifications (on top of the individual more detailed responses).

## 1. Low Variance of Loss and Fairness

We agree that low variance in loss does not inherently ensure fairness and could lead to a degenerate solution. To address the issue of degenerate solution, **Equation (1)** in Section 4.1 combines an ERM term with the fairness regularizer based on variance and to ensure fairness the reducing variance in combination with the usual ERM objective tends to improve the worst group performance without harming the overall performance.

This ensures that the model simultaneously optimizes for accuracy and group fairness.

---

## 2. Fairness Definitions and Metrics

We employ multiple fairness metrics to provide a holistic evaluation:

- **Min F1 per class** and **min recall per class**: Evaluate fairness across classes.
- **Equal Opportunity Difference (EOD)**: Measures fairness across sensitive attributes.
- **Sensitive attribute prediction**: Tests fairness across tasks, such as spurious correlations in the Waterbirds dataset.

For example, on the Waterbirds dataset, our FairLoRA model achieves:
- **ΔF1**: \(18.58 +/- 1.15\).
- **Maximum EOD**: \(37.84  +/- 0.43\) (see Table 2).

---

## 3. Datasets and Domains

The datasets used cover diverse challenges:

- **GeoDE**: A long-tailed dataset with 40 classes and geo-location metadata.
- **Waterbirds**: A spurious-correlation dataset with synthetic sensitive attributes.
- **Aircrafts**: A dataset with high intra-class similarity.

We have clarified group and sensitive attribute definitions in Section 5.

---

## 4. Experimental Setup and Scale

Our experiments span:
- **3 datasets**, **3 architectures** (ViT, CLiP, DiNO), and varying ranks with/without fairness regularization.
- ~200 runs over 3 seeds, supported by hyperparameter searches involving **6,000 experiments**, consuming ~3 GPU hours per experiment.

This further demonstrates the thoroughness of our evaluation as highlighted by the majority of reviewers and addresses concerns about insufficient experiments raised by reviewer 42sa.

---

## 5. Novelty and Contribution

Our contributions include:
1. Introducing fairness into LoRA for vision tasks.
2. Providing insights into fairness-specific challenges, such as non-monotonic behavior with varying LoRA ranks (see Figure 5).
3. Highlighting the importance of evaluating fairness across multiple metrics
4. Exploring the impact of pre-training models, pre-training data and pre-training techniques when used with FairLoRA

---

## 6. Majority Group Performance

Fairness studies often focus on minority groups. However, majority group performance can be inferred from overall metrics. For example, on the Waterbirds dataset:

- FairLoRA improves the best group accuracy from **89.98%** to **93.32%** without harming the fairness objective.

We are happy to add explicit majority group metrics in the appendix, if requested.

---

## 7. Visualization, Clarity and Related Work

We have added examples of visualization of the impact of FairLoRA on the distribution shift as requested by the reviewer, details can be found in - https://openreview.net/forum?id=pB3KeBCnQs&noteId=F5C79RolEe

We have clarified group definitions in Section 5 and expanded discussions on related work in Sections 7.1 and 7.2. Minor visual issues, such as font sizes in figures, will be fixed in the final version.

---

## 8. Future Directions

We thank the reviewers for their suggestions on applying FairLoRA to large language models and segmentation tasks. While beyond the scope of this work (agreed by the reviewer as well), these are excellent directions for future exploration (added to Section 8).

---

## Conclusion

We have addressed all reviewer comments and revised the manuscript accordingly. We strongly believe that the feedback has improved the quality and readability of our paper and thank the reviewers for their invaluable contribution. On that note, we kindly request the reviewers to reconsider their current score evaluations. We are happy to provide further clarifications if needed during the reminder of the discussion period and also make adjustments in the camera ready version.
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Metareview:
metareview:
The paper received mixed reviews from the reviewers. While the authors actively provided explanations and clarifications to the manuscript, some issues still remain (e.g., insufficient experiments on visualization and datasets). Overall, the ratings remain marginally below the threshold for acceptance, and thus the paper is not accepted for publication.