Benchmarking Bias Mitigation Toward Fairness Without Harm from Vision to LVLMs
Authors: Xuwei Tan
Ziyu Hu
Xueru Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: AI Fairness
AI & Society
Utility-Fairness Trade-off
Visual-language models
Primary area: datasets and benchmarks

Abstract: Machine learning models trained on real-world data often inherit and amplify biases against certain social groups, raising urgent concerns about their deployment at scale. While numerous bias mitigation methods have been proposed, comparing the effectiveness of bias mitigation methods remains difficult due to heterogeneous datasets, inconsistent fairness metrics, isolated evaluation of vision versus multi-modal models, and insufficient hyperparameter tuning that undermines fair comparisons. We introduce NH-Fair, a unified benchmark for fairness without harm that spans both vision models and large vision–language models (LVLMs) under standardized data, metrics, and training protocols, covering supervised and zero-shot regimes. Our key contributions are: (1) a systematic ERM tuning study that identifies training choices with large influence on both utility and disparities, yielding empirically grounded guidelines to help practitioners reduce expensive hyperparameter tuning space in achieving strong fairness and accuracy; (2) evidence that many debiasing methods do not reliably outperform a well-tuned ERM baseline, whereas a composite data-augmentation method consistently delivers parity gains without sacrificing utility, emerging as a promising practical strategy. (3) an analysis showing that while LVLMs achieve higher average accuracy, they still exhibit subgroup disparities, and gains from scaling are typically smaller than those from architectural or training-protocol choices. NH-Fair provides a reproducible, tuning-aware pipeline for rigorous, harm-aware fairness evaluation.

Published as a conference paper at ICLR 2026
BENCHMARKING BIAS MITIGATION TOWARD FAIR- NESS WITHOUT HARM FROM VISION TO LVLMS
Xuwei Tan', Ziyu Hu', Xueru Zhang' 'The Ohio State University. Stteens Institute of Technology tan. 1206 laeuosuueee. edu, zhu31@stevens zhang. 12801110007
78 oosu.uuuooo.. edu
ABSTRACT
Machine learning models trained on real-world data often inherit and amplify biases against certain social groups, raising urgent concerns about their deployment at scale. While numerous bias mitigation methods have been proposed, comparing the effectiveness of bias mitigation methods remains difficult due heteroge- neous datasets, inconsistent fairness metrics, isolated evaluation of vision versus multi-modal models, and insufficient hyperparameter tuning that undermines fair comparisons. We introduce NH-Fair, unified benchmark for fairness without harm that spans both vision models and large vision- Saaiuan models (LVLMs) under standardized data, metrics, and training protocols, covering supervised and zero-shot regimes. Our key contributions are: (1) a systematic ERM tuning study that identifies training choices with large influence on both utility and disparities, yielding empirically grounded guidelines to help practitioners reduce expensive hyperparameter tuning space in achieving strong fairness and accuracy: (2) evi- dence that many debiasing methods do not reliably outperform a well-tuned ERM baseline, whereas composite data auggmmtammnnnnnn method consistently delivers parity gains without sacrificing utility, emerging as a promising practical strategy. (3) an analysis showing that while LVLMs achieve higher average accuracy, they still exhibit subgroup disparities, and gains from scaling are typically smaller than those from architectural or rraiiin-protoon ducible, tuning-aware pipeline for rigorous, harm-aware fairness evaluation. Code: https:://ithuut com osu srml /NH-Fair
choices. NH-Fair provides repro-
INTRODUCTION
Machine learning (ML) models increasingly shape high-stakes decisions, raising concerns that they replicate or amplify socictal biases, leading to unfair outcomes across social groups. Various metrics have been proposed quantify the model unfairness/bias such risk disparity (Hashimoto et al. 2018), demographic parity (Dwork et al. 2012), equal opportunity, equalized odds (Hardt et al.
2016), overall accuracy parity (Berk et al. [021] ) and max-min fairness (Lahoti et al. 2020), yet
each captures different notions and priorities can depend on practical considerations.
Fairness interventions span pre-processing (Qraitem al. 2023 Sagawa al., 2020 Pang et al.
2024 Jang et al., 202T), in-processing (Madras et al. 20188] Xu et al. 2021] Chuang and Mrouch
2021 Park
2022 Zafar et 2019 Zuo et 22024) and post-procest processing (Hardt al. 2016
Yin et al. 2024 Dehdashtian et al. 2024; Jung et al. 2024 Tan et al. (2226) methods. But they often
add complexity, require extensive hyperparameter tuning, or degrade overall accuracy (for example, model may degrade performance for some groups to meet fairness criteria like demographic or accuracy parity). safety-critical domains such as healthcare, sacrificing performance for fairness violates ethical principles of beneficence and non-maleficence (Beauchamp and Childress 1994). Motivated by this, growing line work seeks fairness without harm, i.e., improving group parity without materially reducing performance for any group (Ustun al. 2019 Martinez and Bertran 2019; Yin et al., 2024). Instead of solely enforcing fairness constraints across different groups, these approaches ensure that model performance for every group does not deteriorate.

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Published as a conference paper at ICLR 2026
Despite significant progress, existing approaches were often designed for different problem settings and evaluated in inconsistent, limited environments, making it difficult to assess their general applicabilityy. In particular, several key questions remain unanswered:
Comparability under a standardized protocol. In faimess research, there are usually inconsistent experimental settings when comparing different methods, e.g.. arbitrary choice of optimizer, different hyperparameters, and insufficiently trained baselines. These inconsistencies prevent us from accurately assessing the real utility and fairness performance of state-of-the-art debiasing methods. How do these methods perform in utility, fairness, and overhead, under the same setting with comprehensive hyperparameter sweeping? And can they outperform carefully trained ERM? What makes ERM strong -nd how far can it go? There is a practice-researee gap: industrial workflows rarely explore the full suite of fairness algorithms and instead prioritize hyperparameter optimization (HPO), like using Ray Tune (Liaw el al. (0188) or Optuna (Akiba et al. 2019), on incumbent models. Yet, exhaustive HPO is computationally expensive. Can we bridge this gap by revealing which training decisions, such as optimizer, model depth, augmentation, most impact fairness? In addition, if we start from a powerful ERM via principled tuning and selection, can conventional group- disparity mitigations still deliver faimess without materially compromising accuracy? Does simply scaling model size lead to better fairness? Where do foundation and multimodal models stand? In the era of foundation models and large pretrained models, are these multi-modal models already fair enough compared with specifically trained models due to pretraining on larger training data with larger model size?
Although some benchmarks have been proposed to make the comparison of previous methods, like MEDFAIR (Zong et al.) 2023), FFB (Han et al., 2024), and ABC (Defrance et al. 2024). They often focus on specific domains, omit recent methodological advances, or suffer from limited hyperparameter tuning and dataset diversity. For example, MEDFAIR is restricted to medical datasets and does not evaluate fairness in general vision or multimodal contexts. FFB includes primarily older methods, omitting recent advances in representation learning and data-centric approaches, and lacks sufficient hyperparameter tuning, which may result in suboptimal models. ABCFair focuses on tabular datasets only and uses fixed hy perparrameter settings, which limits scalability and may misrepresent method performance. The questions mentioned above not yet fully addressed. Moreover, existing benchmarks (Xia et al. 2024 Jin et al. 2024a) generally treat classical vision and emerging multimodal models separately, limiting our understanding of how multimodal pretraining affects fairness relative to other models. We introduce these benchmarks in Appendix AA.4
To bridge this gap, we propose NH-Fair, comprehensive benchmark for fairness without harm in complex image and multimodal settings. NH-Fair unifies evaluation across classical vision models and vision-language models, providing broader insights into fairess comparisons of architectures, pretraining strategies, and model scales. Beyond benchmarking both classical and recent fairness algorithms, we systematically evaluate the role of training choices, the overhead of existing methods, and the performance of state-of-the-att LVLMs. Our goal is to provide not just benchmark, but actionable insights for developing and deploying fair ML systems. We summarize contributions and key observations with practical implications for practitioners:
ERM and Training Choices Matter. We investigate how training choices affect fairness.
Observation. Prior work often overlooks hyperparameter tuning, like optimizer and learning rate choice. We show that fixing hyperparameters across methods and datasets can yield unfair comparisons. Our findings challenge this practice, where some papers claim "state-of-the" art" fairness without sufficient evaluations, and underscore the need for more equitable and transparent evaluation protocols in future rescarch.
Takeaway. Optimizer choice (e.g., SGD, Adam, AdamW, Adagrad) and its learning rate affect both fairness and utility. We recommend focusing on tuning resources here, as these settings have clear impact. This also applies to selecting the correct pretrained weights, while model depth, batch size, or weight decay have less impact on fairness.
2. Revisiting Mitigation Methods. We offer an extensive comparison of recent and classic bias mitigation algorithms, assessing both faireess and accuracy to determine if current methods can achieve equitable performance without compromise.
Observation. Most faimess specific algorithms do not significantly outperform a carefully tuned ERM when utilities are accounted for. Data augmentation a simple yet effective strategy that often improves both fairness and utility.
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Published as a conference paper at ICLR 2026
Takeaway. In practice. prioritize augmentation strategies before exploring wide range of spe- cialized algorithms; this pathway most often achieves fairness without utility loss or overhead. 3. LVLMs Are Not Inherently More Fair We extend evaluation to multi-mode models and LVLMs to probe whether large-scale, diverse pretraining reduces disparities or propagates stereotypes. Observation. Despite greater data diversity and larger capacity, LVLMs still exhibit fairness issues comparable to task-specific vision models. For example, the Llama series, while achieving strong headline accuracy, remains highly susceptible to datasct-specific biases. In addition, the model scale shows weak correlation with fairesss improvements.
Takeaway, Practitioners concerned with fairness should prioritize evaluating different architec- tures and pretraining choices rather than relying on scaling up models alone.
2 PROBLEM FORMULATION, FAIRNESS WITHOUT HARM
In this paper, we evaluate each MI model based on two criteria: 1) performance disparity across different groups, and 2) negative impact on model performance caused by fairness interventions. Therefore, we consider the fairness without harm problem formulation, which aligns with our goal.
Group fairness notion Consider a dataset of n samples, where sample i is represented a triple (I,, De, a.) from the joint distribution P(X, A).. Here, 2ạ E Rd is the feature vector, y E y the label, and a, E AA is the sensitive attribute (e.g., gender race). We learn model h : R + y that achieves strong predictive performance while satisfying a fairness constraint. We evaluate performance using risk function R Y Y R+ that quantifies the discrepancy between predictions and labels. To assess disparity across groups, we consider the following fairness criteria.
Overall Accuracy Parity requires that the classifier's accuracy be equal across groups:
P[A(X) = A = a] F[M(X)=
A d Va, a' A.
We use the accuracy gap between the two groups to denote overall accuracy parity in this paper. Demographic Parity, also known as statistical parity, requires that the classifier's decisions be independent of the sensitive attribute:
(MMX) y A a] P(M(X) = y 1 d'd,
Va, a' E EA, y V.
(2)
Equalized Odds requires conditional independence of l(X) and a given the true label, e.g., the true positive rate and the false positive rate are equal across groups:
PlM(X) Y v, a} (P((())= Y A ddd,, Va, a' E A, E... (3)
Max-Min Fairness uplifts disadvantaged groups by minimizing the worst group risk:
Ex v()(
max a
We do not consider individual faimeess Dwork et al. 2012) or counterfactual fairness (Kusner et al., 2017]. The former requires a well-defined similarity function between individuals, which hard to specify in images, while the latter usually assumes access to causal graphs, which are unavailable in most datasets. Given these constraints, we focus on group fairness.
Fairness without harm Given group fairness notions defined in Sec. 2 enforeing these fairness constraints inevitably harms model performance. Consider Demographic Parity (DP) example, the base rates outcomes differ across groups (P' [x() = A a) + IP(h(X) =
a'), V, a, E A.), enforcing independence between h (X) and A may require distorting predictions to align with group-agnostic rates. Suppose we have two groups and the optimal unconstrained classifier for group 0 satisfies P [h(X) y A 0 = Po- To achieve DP, we must enforce A 1
for some value of which could force p to deviate from the group- seeu-sceecfiic optima Pu. thereby increasing overall risk. In extreme cases, this might result in trivial classifier (e.g. h(X) 1 for all data) that satisfies DP but incurs maximal risk. Thus. fairness interventions risk creating a "race to the bottom," where fairness is not achieved by elevating disadvantaged groups by reducing accuracy for all groups. To avoid this, adopt the
P ([((X)=
A 0] (
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Datasets
Human
Medical
Natural
ColebA, UTKF aos, Farraace, Facet
Fitepatrik17kk
Methods Image-Texe Text Match
Classification
Image-Text-to-Tew
Data-Centric Methods
photo of person
R
photo or femole?
"A photo of person with hair."
M
Algorithmic Methods
Tuning
LLaVa
Olwn
Gemma
Llama
DTO
FWH Selection
Similarity: [0.2,
Answer: Male.
Selection
Evaluation
Experiments
Metrics
Supervised Training
Zero-shot Prediction
Utility
Overall
Equalized Odds
Classification
Accuracy Parity
Hyperparameter Study
LVLMs Model Size Study
Max-Min
Demographic
Overhead
Fairness
Parity
Figure 1: Overview of NH-Fair, evaluating fairness across domains, tasks, models, and methods.
principle of fairness without harm, which augments group fairness with no-harm condition. herm denote the baseline classifier trained via unconstrained empirical risk minimization (ERM);
Berm arg min
R(h(aa)..:).
NEH
The no-harm criterion requires that for every group E A, the risk incurred by our faimess-cnhanced classifier does exceed that of the baseline:
Ex.YIA= [(((())))) Ex,yIA=a ((hamm()))))
Va A.
(6)
3 NH-FAIR BENCHMARK
3.1 DATASETS
We evaluate fairness algorithms on seven publicly available datasets spanning facial attributes, medical imaging, and spurious correlation tests: CelebA, UTKFace, FairFace, Facet, HAM10000, Fitzl7k 17k and Waterbirds. Table summarizes target tasks and sensitive attributes used in our evaluation. Due the huge computational resource needs, did not exhaustively (e.g., gender in UTKFaaee.. Instead, we focused on attributes that exhibited the clear disparity in model predictions, enabling more effective comparison of existing fairness methods.
all available sensitive attributes
Criteria for Dataset Selection. By definition, fairness evaluation requires demographic information (e-g., race, gender, or age) measure disparities in model performance across subgroups. Therefore, our primary selection criterion is that datasets must include explicit demographic annotations. This ensures that fairness metrics are meaningful and grounded socially relevant groups. Following this principle, we include the first six datasets. In addition, we considered: application domains, potential sources of bias, such as class imbalance or spurious correlations, and relevance to prior fairness studies. Waterbirds represents pragmatic exception: it lacks demographic labels but has been widely used in fairness research due to its spurious correlations (e.g., background vs. object) resembling biases. Including it enables consistency with prior work (Reddy et al. 2021 Dehdashtian et al. 2024 Qiang et al. 2024) and provides a test for algorithms in non-demographic bias settings. However, suggest carefully using domain generalization datasets (c.g., Waterbirds, Colored MNIST) in fairness evaluations since these datasets might be over-simplistic and cannot reflect the real challenge of the MI. fairness issue. We discuss this dataset in Section 4.2
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Potential Sources of Bias. We use datasets that may introduce bias from multiple sources, including image quality, class imbalance, and spurious correlations. Importantly, each dataset often reflects more than one of these factors simultaneously, making it difficult to attribute disparities to a single cause. For this reason, we do not perform dataset-level bias source analysis.
Table I: Overview of the seven image datasets, detailing the number of samples, classification target, sensitive attribute(s), and approximate imbalance ratios expressed as percentages. In this table, M denotes Male, F denotes Female, W denotes White, B denotes Black, LH denotes Latino Hispanic, EA denotes East Asian, SA denotes Southeast Asian, IN denotes Indian, ME denotes Middle Eastern, N denotes the negative class, and P denotes the positive class.
Dalasel
A Sumples
Target
Sensitive
Target Ratio
Sensitive Ratio
CelchA [Lán of al. 2015
200k
Waxy Hair
Gender Rice
68% (P) 32% (M)
41.7% (N): 58.3% (P) 57.0%% 42.4% (O)
UTKFace
2017
Gender
52%
(F)
19.15
1% 15.3%
FairFace Karkkainen and Jon,
100k
Fihnicity
Gender
14.25 (FA): 12.55 (SA)
53% (M) :475) (F)
14.2% (IN): 10, (ME)
Facel (Gintahon et at 2024
30k
Visible Face
Gender
67% (N) 33% (P)
76% (M) 24% (P)
HAMT000 Mtrrnn the 2019
10k
Malignant
Age
86% (N) : 14% (P)
71% (Young): 29% (Old)
Fitz17k
Grih
Malignant
Skin Type
86.5% (N) 13.5%%
48% (Light): .2%% (Dark) Train: 74.1% (N) 25.0%
Waterbirds
Sagawa" et
Species
Background
Test: 77.8% (Waser): 22.2%6 (Land)
Thain: 76.8% :22.25 (P)
Text: 50% (Watert: 50% (Land)
3.2 METHODS
We evaluate a diverse set of 12 baseline algorithms, spanning both fairness-specific and general- purpose approaches. To organize them, we group methods into two broad categories: Data-Centric and Algorithmic methods. Data-Centric methods focus on modifying the input distribution through data augmentation and sampling strategies, including RandAugment (Cubuk et al. 2020), Mixup (Zhang et al. 2018). Resampling (Buda et al.] 2018 [Sagawa* al. 2020), Bias Mimicking (BM) (Qraitem et al. 2023), ) and FIS (Pang et al. (2024). Algorithmic methods, by contrast, alter the
training process through adversarial training fairness-aware objectives. These include Decoupled Classifier (Ustun et al. 2019:) Wang et al.) 2020). LAFTR (Madras et al., 2018). FSCL (Park et al.
2022), GapReg (Chuang and Mrouch| 2021). MCDP (Jin al. 20244b. GroupDRO (Sagawa* et al. 2020). DFR (Kirichenko et al. (202)), and OxonFair (Delaney et al. 2024). Together, these methods span both established baselines and recent state-of-the-art techniques and include pre-processing, in- po-proceesing. and post- post-processing methods. Note that we do not consider causal faimnss approaches since most of them assume access causal graphs or intervention variables. However, these assump- tions are rarely feasible in our datasets. A more detailed description of each method is provided in Appendix B.3.
Beyond supervised models, we also evaluate zero-shot predictions from multi-modal models such as CLIP (Radford et al. 202 and BLIP2 (Li et al. 2023), as well as LVLMs, including LLa VA v1.6 (7B, 13B, 34B) (Liu et al. 2024), Owen2.55VVL (7B, 32B, 72B) (Bai et al. 2025), Gemma 3 (4B, 12B, and 4-Scout-109B) (Grattafion] let al. 2024 Meta 2025) For CLIP and BLIP2, construct prompts for rext-image matching tasks using pairs of positive and negative labels (e.g.. "A photo of person with non-wavy hair." vs. "A photo of a person with wavy hair.). For LVLMs, we frame image-text-to-text tasks that elicit binary responses, using prompts such as "Is the person in the photo wavy-haired? Answer 'Yes' for wavy hair, 'No' for non-wavy hair." Full prompt templates are provided in Appendix Table g
27B) (Team et al.) 2025). and Llama models (3.2-11B
3.3 MODEL SELECTION
Selecting the best- best-performing model affects not only overall utility but also disparities across sub- groups. Conventional selection methods, which prioritize average accuracy or loss, often reinforce majority-group performance while neglecting minorities. This motivates model selection strategies that explicitly balance utility and fairness. Prior work has explored approaches such as Minimax Pareto Selection (Martinez et al. 2020) and Distance to Optimal (DTO) (Han et al.) 2022 to identify fair models. In this work, we adopt a DTO- strategy to establish a strong ERM baseline, considering both utility and group faimess. We then take the selected ERM model as a baseline and introduce a fairness-withe wwth-wtthhatttttee harouuttthammmmmmm (FWH) procedure to assess whether alternative methods can improve fairness without materially degrading utility compared to the well-established ERM.
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Sensitive Group 2 Accuracy
Utopla Point
Sensitive Group Acccuracy
(Tammeo Alte J
0.6
Seletall ERM
Sejecho ERM
by fumily
Optigg Leieen
0.75
2.25
Best Group?
0.-
0.5
07
0.75
68
925
20
0.6
0.6
0.7
0.55
0.6
0.25
03
005
Sensitive Group 1 Accuracy
Sensitive Group Accuracy
(a) DTO- ERM model selection
(b) Fairness-Without
Model Selection
Figure 2: Two-stage model selection. (a) First, each green dot represents a candidate ERM model's performance on two sensitive groups. We select the ERM model (orange circle) whose performance is closest to the utopia point (red star) in Euclidean distance, (b) With the ERM baseline established, classify models trained by bias-mitigation methods into four zones based on how their subgroup performance compares to the ERM. Starting from the Optimal Zone and moving counterclockwis we check whether any model is located in the shaded region, which demonstrates improved fairness.
1) DTO-based selection. The DTO strategy identifies the ERM model that minimizes Euclidean distance to utopia point, where each subgroup achieves its best observed performance. This approach aims to find the model that maximizes overall performance while minimizing disparities across subgroups. By establishing this model as the optimal ERM baseline, we provide reference point for evaluating fairness-aware models. As shown in Figure 2a the red star denotes the utopia point, where both subgroups reach their respective maximum performance. The model with the smallest DTO value (the shortest distance to the utopia point) is selected as the ERM baseline.
2) Fairness- heirness-without-bee without-harm selection. Given the optimal ERM, we evaluate other methods by comparing subgroup-speciffe accuracies. Based on the group accuracy of the selected ERM model, we categorize the models from other methods into four distinct zones:
Optimal (Fairness Without Harm): Models here already achieve better utilities for both groups compared with the established bascline ERM. A smaller accuracy gap desirable to show fairness.
Thus, we select the model with the smallest accuracy gap.
Sub-optimal (Fairness by Compromising): Models reduce disparities by decreasing the advantaged group's 's utility while improving the disadvantaged group. If no candidates exist in Optimal zone, we select the model here with the smallest accuracy gap to evaluate how it equalizes two groups. Degradation (Fairness by Harming Both Groups): Models are undesirable since they degrade performance for all groups, achieving faimess at the expense of overall utility. We select a model with the smallest L2 distance the optimal ERM to maintain necessary utility rather than fairesss metrics, since the models can significantly decrease utility (random guess) to equalize accuracy. Unwanted: Models in this zone widen disparities by benefiting the advantaged group and harming the disadvantaged group, which exacerbate unfairness and are not considered.
When evaluating fairness-aware models against the optimal ERM, we follow the selection order: Optimal + Sub-optimal + Degradation. This ensures fairness while minimizing performance trade-offs. Using this strategy, we select the model that best meets the wiithut- harm criteria.
4 RESULTS
Implementation. For all methods, we independently search for the best hyperparameters on each dataset. Specifically, we search over optimizers (SGD and Adam), learning rates, weight decay values. and weethod-specific hyperparameters. Implementation details are provided in Appendix [.4 and B.5 In total, we spent over 10,000 A100 GPÚ hours to obtain these benchmarking results.
Metrics. We use Accuracy (ACC) to evaluate utility on most datasets, while AUC is adopted for disease prediction tasks, since medical datasets often exhibit class inbalances. For faimess,, report

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Published as a conference paper at ICLR 2026
four metries: Overall Accuracy Parity (Gap 1). Max-Min Fairness (Worst f), Demographic Parity (DP 1), and Equalized Odds (EqOdd f). They are introduced in Appendix B.2
4.1 HOw TO TUNE A FAIR ERM AND WHY TRAINING CHOICES MATTER
This part is motivated by two observations. First, ML practitioners rarely conduct extensive testing of different fairness- specific algorithms and instead focus on tuning existing models. Yet exhaustive hyperparameter search is computationally expensive, raising the question: which training choices should be prioritized for fairness- -eensssssseeiiivvee applications? Second, many prior studies report fairness or utility gains while keeping core training parameters fixed across baselines and datasets. But can such practices truly yield fair and reliable comparisons?
To answer these, we conduct systematic study under different training setups (see Appendix C.I. We find that some choices, most notably initialization (pretraining vs. scratch) and optimizer, consistently impact the Mairness-utille trade-off. For example, pretrained models often improve utility without harming disadvantaged groups, and the right optimizer (c.g., SGD on CelebA, Adam on 1itz17k) can shift both utility and subgroup performance. In contrast, factors like batch size, weight decay, or model depth have weaker or inconsistent effects. These findings suggest that impactful training choices could be prioritized during HPO. In addition, fixing such parameters across baselines and datasets can lead to biased comparisons. It underscores the need for more equitable and transparent evaluation practices in future fairness research.
4.2 REVISITING MITIGATION METHODS
well-tuned ERM base- line could match sur- pass fairness-specifie algo- rithms. Table reports util- ity and fairness outcomes for sigglemm models across seven datasets. For each mataset-method pair, results are averaged over five runs, with the best ERM model selected under DTO criteria and the best models for mitigation meth- ods selected under FWH criteria. Table 3 extends the comparison to multi- modal models. Despite be- ing treated as a "basic" base- line, well-tuned ERM, selected via DTTO consistently performs competitively across fairness metrics (Gap, Worst, DP, Eqodd). Figure 3 shows that ERM performs competitively with, or sometimes outperforms, specialized fairness methods under FWH selection, and no single mitigation approach dominates across all datasets. We further conduct a Friedman test across all datasets, followed by Nemenyi post-hoc analysis. We visualize the results using Critical Difference (CD) plots in Figure 4 where methods connected by horizontal bar are not significantly different under the Nemenyi test.
[Higher Better)
Seore
score
MEAM
massamping DDDcoupped Glppph MGroupDRO
BRandrug maM
mLAFTR #FSCL
gMCaP
gara
HHbup
IFI
Valle
Worst
GAP
Eqp-dd
Fairn ess Metric
Figure 3: Comparative Analysis of Bias Mitigation Methods. OxonFair is excluded from here due to missing FairFace results.
Data augmentation is a simple yet effective path fairness without harm. Our results show that fairnsss improvements do not inherently require sacrificing accuracy. Prior studies like Dutta et al. (2020) established the theoretical possibility of achieving both. In the paper, RandAug, though not designed for bias mitigation, improves both fairness and accuracy across multiple datascts, empirically demonstrating that faimess improvements do not always require a trade-off in model performance. This suggests that increasing data variability can naturally mitigate biases, offering a lightweight and practical strategy for aaiee training without the need for explicit fairness constraints.
The fairitsssarresss trade-offs pronounced in regularization-bane based methods. Both GapReg and MCDP account for explicit fairness constraints in the loss functions, directly penalizing subgroup disparities. This design explains why they consistently achieve strong scores on fairness metrics
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Table Average utility and fairness results (standard deviations are reported in Appendix Table 20p. Results better than ERM are highlighted. We also report the number of selected models located in each zone by the FWH selection in (obbimalSSu-optimall set, and further verify these findings on the test set.
Unwanted" on the validation
Dataset
Metric
ERM
RandAng
Mixup
Resampling
BM
FIS
Decoupled LAFTR
FSCI
GapReg
MCDP
GroupDRO
DFR
OxonFair
ACC
86.57
86.72
85.61
S6.35
85.93
83:05
86.35
86.55
85.61
85.62
80.26
66.12
$6.58
36.19
Wont
B3.76
83.89
82.74
83.44
82.86
79.33
83.46
83.67
82.56
83.17
77.13
K3.50
83.78
83.63
CelebA
EgOld
61.91
6.76
6.80
87.08
6,98
75.79
8.94
80.59
6.91
81.15
7.52
6.31
6.74
DP
67:20
67.37
70.83
67.39
66.91
66.84
66.68
66.90
69.72
75.91
93.11
66.41
67.30
65.10
ACC
92.75
93.19
92.62
92.70
93.33
91.97
91.68
93.17
93.52
92.53
92.49
92.45
92.73
92.36
Wont
91.78
92.19
91.55
91. (60
92.27
90.91
90.84
92.05
92.62
91.70
91.63
91.41
91.60
91.11
UTKFace
EqOid
Gap
97.62
2.36
97.62
2.34
97.61
2,50
97.
2,56
97.39
2.47
97.51
2,48
97.43
1,47
97.44
2.61
97.44
2.10
98.10
1.91
96.04
2,00
97.06
2.44
97.39
2.63
96.11
2,91
DP
66.76
65,40
65.31
67.03
66.06
Wosst
66.34
67.69
64,50
64,51
65.20
64.59
66.61
65.60
64.64
64.12
65.62
65. 65,22
62:45
FairFace
EgOod
Gop
96.22
01.87
94.14
1.44
96.55
1.93
96.83
1,90
95.70
0.97
95.88
1.53
95.14
I.87
94.73
1.76
97.05
1.66
96.15
1.92
97.85
0.90
96.31
0.60
95.81
1.59
DP
97.61
97.55
97.62
97,80
97.30
97.40
96.88
96,86
98, 10
97.51
98.50
97.47
97.43
ACC
67.55
67.83
67.86
67.56
65.87
77.60
67.35
70.74
67.79
67.91
67.20
68.09
Woust
64.94
64.54
64.13
63.53
62.60
68.60
64.21
63.10
Facet
EqOld
Gop
96.47
4.31
96.68
3.78
97.50
4,37
94.37
96.32
4.18
98.10
5.33
88.04
6.17
96.38
2-82
96. 50
98.92
98.23
4.84
95.76
96.82
4.93
X3 40
5.21
DP
95:40
95.91
96,71
93.96
95.09
97.84
87.30
95.00
95.66
98.73
98.46
93.16
96.09
83.95
AUC
88:35
89.09
86.51
87.75
89.54
85.97
87.87
86.71
89.40
84.97
82.96
87.66
87.06
88.46
Worst
84.67
84,67
82.31
84.77
$6.49
82.98
84.04
81,68
85,89
82.57
80.29
83.98
82.49
83.83
HAM10000
EgOdd
86.79
4.11
88.43
91.14
91.84
94.05
77.52
93.28
99.52
90.94
91.25
99.27
DP
81.48
84.73
88.54
85.05
75.41
88.55
75.15
91.00
77.64
96.74
99.58
83,86
84.65
99.34
AUC
89.77 74
91.29
90.62
90.76
91,02
88. 39.34
89.65
90.95
90.71
89 59
91.65
90.72
89.99
89, 39.56
Wons
88.39
90.15
89.38
88.99
89.93
87.02
88.45
89.67
89.77
88.52
90.49
90.06
88.57
$8.40
Fitz17k
EgOid
Gap
94.92
2,92
95.61
2,51
96.20
2.43
96.27
3.62
94.99
2.34
95.17
3.06
94.09
2.55
2,87
97.45
2.22
1,84
95.68
2.87
94.36
1.92
2,91
99.26
3.06
94.46
94.53
94.51
93.31
95.60
94.06
94.46
96.42
96.14
95.24
95.26
ACC
85.63
86.09
87.67
87.35
58.20
83.72
74.64
85.72
86.83
86.45
85.98
85,46
89.83
90.27
Wonst
84.20
84.52
85.99
84.85
85.96
82.67
64.45
83.94
86.28
K5.77
84.83
84.45
89.09
89.52
Waterbirds
EyOöd
Gup
66.5
2.87
68.99
3.14
81,42
3.36
90.87
1.98
77.21
4.48
65.89
2,09
47.31
20.38
68.22
3.56
90,00
1.10
87.45
1.47
72.97
2,31
67.31
2,02
97.79
1.47
94.99
1.50
Dp
77.67
77.25
86,00
90.93
$1.78
75,30
$2.30
77,47
91.39
76.91
98.61
97.38
Selection
FWH
Valitation
Test
700/000
710100
3/0
2
0/2/2
3
3/2
211/100
0/0/6
11:000
11122
3
030000
1
600100
0,215
2/0/4
111210
2131210
01010
1
010/600
1/12//2
Match
7/7
3/7
3/7
3/7
2/7
(a) Utility
(b) Worst
(c) Gap
NCM
35%
147TS
(d) EqOdd
(c) DP
Figure 4: Critical difference plots comparing methods across five metrics. Lower ranks indicate better performance. OxonFair is excluded since it does not support multi-class classification on FairFace.
such EqOdd and DP across datasets. However, the fairness penalty can pull decision boundaries away from the utility-optimal surface, which often results in lower accuracy and occasionally lower Worst-group accuracy. They highlight the classic faimnss--ttili trade-off: faimess comes at the expense of overall predictive strength. In contrast, the contrastive learning method FSCL, which encourages representations of the same class cluster across different groups, delivers strong
8

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Published as a conference paper at ICLR 2026
faireess improvements while keeping accuracy competitive, implying that learning fair and robust representations is also a promising path to fairess without harm.
Rethinking spurious correlation datasets for fairness evaluations. In prior studies (Dehdashtian ct al. 2024; Qiang et al. 2024; Reddy et al., 202I), datasets with spurious correlations are often used for fairness evaluation since spurious correlations resemble the types of confounding factors that lead to fairness issues. We noticed that, in 2 many methods (e.g., Mixup, Resampling, BM, OxonFairr..) can achieve both higher utility and fairness on Waterbirds, but it is more difficult to achieve such a gain on fairness datasets with socially meaningful group disparities. This raises a potential concern: spurious correlation might be easier to resolve than sairees--ppeiffe distribution shifts. Fairness involves systematic performance gaps across protected groups, which are typically more subtle and persistent than background-ohjeet correlations. Over-reliance on datasets like Waterbirds may therefore underestimate the difficulty of fairness challenges and overstate algorithmic effectiveness. While useful for studying robustness to spurious correlations, such datasets are not ideal for fairness evaluation. We recommend using datasets that provide coherent and well-justified data groupings. preferably with clear, socially meaningful attributes (especially for readers from social science domains), and exercising caution when incorporating other datasets with data shift (e.g., domain generalization benchmarks) for fairness assessment.
4.3 FAIRNESS IN MULTI-MODAL MODELS
We showed that even the best-tuned vision models leave non-trivial subgroup gaps. A natural hypothesis is that these gaps reflect limited data diversity and model capacity. Here, we evaluate pre-trained multi-modal models, which are trained on massive, diverse datasets and are expected to generalize better. Specifically, we consider two families: (1) image- text matching models such as CLIP, BLIP-2, -2 and two CLIP-based debiasing variants CrairerCiPPDhhdashhttin Dehdashtian et al. (2024). SFID Jung al. (2024)); and (2) LVLMS such as LLa Qwen2.5-VI., Gemma 3, and Llama. All models are evaluated in zero-shot predictions. Fig. [ visualizes the joint landscape of utility and parity, where Qwen2.5-VL 72B lies closest the outer envelope across datasets, indicating the best overall balance between high accuracy and low disparity among the evaluated LVLMs
Fairness persists as challenge. From B we found that fairness outcomes diverge sharply between im- age-text matchers and newer large models. BLIP-2 and CLIIP still ex- hibit sizable subgroup gaps on CelebA and Waterbirds. Even though debi- asing methods like FairerCLIP and SFID can use the validation set to tune the model, they still don't 't signif- icantly address the fairness issue. As for LVLMs, despite being trained on massive, heterogeneous corpora, mul- timodal models do not universally out- perform carefully tuned unimodal baselines in either utility or fairness. On relatively balanced datasets like UTKFace, LVLMs such as Qwen2.5-VI. and Gemma-3 deliver strong accuracy while keeping subgroup gaps small.
Gap
1.6
Laak 1:6
S.ar 1098
Sesut 1040
Fack
Turfaa
Fsoe
Terfhc
Figure 5: LVLM performance using ACC and 100 Gap. Other metries are presented in Figure II
However, on more challenging datasets (CelebA and Facet), they exhibit subgroup disparities, often with worse worst-group accuracy than ERM and sometimes larger accuracy gap for certain models. These results emphasize that multi-mode models still inherit and sometimes amplify fairness challenges rather than resolving them.
Scaling is not enough. We also studied different LVLM sizes to evaluate whether larger LVLMS could achieve better fairness our tasks (Appendix Table [6] simplified in Table 4. Increasing LVLM size improves average accuracy but does not consistently resolve faimess gaps. Larger models (e.g., Gemma--3-27B Llamaa.2-90BB achieve higher average accuracy compared to their smaller counterparts, yet subgroup disparities (Gap) remain non-trive non-trivial and in some cases even widen. The faireess gains from scaling are much smaller than those obtained by switching to different model

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Published as a conference paper at ICLR 2026
family, suggesting that the training protocol plays decisive role in faimess.. Hence, in faimess- sensitive applications, we recommend first exploring model choice before allocating resources to scaling.
Table 3: Comparison of multi-modal models. Standard deviations are in Appendix Table 21 We omit HAM10000 and Fitz17k for LVLMs, since they do not yield calibrated probabilities for AUC.
Fairer- CLIP
CLIP- SFID
LLaVA-L6
Qwen2.5-VL
Gemma3
Llama4
Dataset
Metric
ERM
RandAug
BLIP2
CLIP
34B
72B
27B
Scout
ACC
N6.57
86.72
47.38
74.07
73.78
72.05
44,83
68.76
74.04
83.71
Worst
83.76
83.89
36.82
67.43
67.32
66.36
32,69
66.02
72.18
80.54
CelebA
Gap
6.76
6.80
18.09
15:97
15.56
13.69
20.75
4.70
4.47
7.62
EgOdd
81.91
81.73
97.24
83,72
83.79
95.70
97.41
92.69
92.76
84.81
DP
67.20
67.37
95.90
81.32
81,06
93.23
91.92
91.71
74.53
71.52
ACC
92.75
93.19
94.23
96.72
96.79
96.70
97.12
96.78
97.25
97.02
Warsi
91.78
92.19
94,00
95,90
96.05
96.03
96.34
95.76
96.89
96.29
UTKFace
2.26
2.34
0.45
1.90
1.72
1.55
1.81
2.36
0.85
1.70
EyOdd
97.62
97.62
99.24
97.96
98.17
98.36
98.16
97.59
99,20
98,27
DP
94.55
94.83
95.61
93.91
94.27
94.96
95.22
94.33
95,28
94,90
ACC 66.76
68.37
52.57
57. 36
56.81
52.73
66.91
65.57
$9.40
56.52
Worst
66.34
67.69
50.74
57.20
56.41
51.59
66.04
64.73
56.71
53.53
FairFace
Gap 0.87
1.44
3.46
0.34
0.86
2.40
1.84
1.79
5.71
6.35
EqOdd
96.22
96.14
93.86
97.16
96,78
97.14
96.22
96.26
95.13
96.54
97.61
97.55
98.36
97.57
97.16
ACC 67.55
67.83
41.16
33.10
33.17
33.26
58.14
68.41
57.75
67.54
Worst
64.94
40.40
31.43
57.9
63.79
57.25
64.61
Facet
Gap
4.31
3,78
3.22
7.13
6.32
7.37
0.72
6.04
1.42
3,83
EqOdd
96.47
96.68
93.52
98.72
98.82
99.75
95.00
97.04
96.22
97.55
DP
95.40
95.91
94.10
98.92
99.00
99,80
93.91
96.15
94,56
96.28
ACC 85.63
86.09
52.30
78.05
77.77
75.70
86.95
92.41
90.77
88.62
Worst
84.20
84,52
39.18
74.53
73.49
72.81
81.26
91.27
88.97
85.74
Waterbirds
Gap
7.04
8.56
2.28
3.59
EgOdd
66.53
68.99
68.24
73.96
73.19
95.57
80.88
94.03
92,99
89.38
DP
AUC
88.15
$9.09
38.87
52.15
51.88
52.53
84.67
HAM10000
Gop
4.11
4.59
6.89
4.14
3.12
2.24
EyOdd
DP
X2.22
84.73
97.89
99.09
98.94
98.32
91.29
69.92
Worsi
88.39
90.15
66.46
69.78
69.85
69.35
Fitz17k
Gap
2.51
EqOdd
94.92
95.61
97.06
89. 87
87.95
85.88
DP
94.46
94.53
98.40
95.03
93.02
92.19
Table Effect of LVLM model scale on accuracy and fairness metries.
LLaVA-166
Qwen2.5.VV
Gemma 12B
Llama
Dalaset
Metric
7B
13B
34B
7B
32B
72B
4B
27B
3.2.
3.2.90B
4 4-Scout 109B
ACC
68.16
69.87
70.79
T7.36
72.72
78.39
67.33
73.48
75.84
74.54
76.03
78.68
66.10
66.55
66.86
75.65
72.60
Average
Gap
3.89
5.70
7.30
2,89
7.66
3.43
10.53
2.09
3.21
10.08
11.09
5.05
EqjOdd
87.91
93.71
94.98
91.32
DP
87.05
88.60
91.92
91.94
96.16
94.88
86.31
90.47
90.94
84.66
87.91
90.03
CONCLUSION
In this study, we introduce NH-Fair, rigorously curated benchmark for evaluating fairness inter- ventions in image models, and show that AI fairness issues remain challenging in computer vision domains, even as new methods are proposed and model capacity continues increase. A carefully tuned ERM with the hyperparameter search often rivals specialized debiasing methods. It highlights crucial role hyperparameter tuning and model selection in achieving fairness without harm. We further find that utility need not be sacrificed: data augmentation can deliver simultaneous gains in accuracy and subgroup parity. In addition, large vision- language models are not exempt from fairness issues. Their pre- pre-training choices still imprint spurious correlations that widen gaps. By releasing NH-Fair and all accompanying code, we aim to make fairness results reprodueible and to provide the community with solid baseline on which to build more robust, bias-awan methods.
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ETHICS STATEMENT
This work focuses on evaluating fairnsss in vision and multimodal models. While our study does not involve direct interaction with human subjects, it makes extensive use of publicly available datasets with demographic information. We acknowledge that these datasets may contain label noise, spurious correlations, or demographic imbalances, which themselves reflect broader social biases. Demographic prediction tasks (e.g., gender or race classification) are used solely for academic analysis of fairness interventions and are not intended for deployment or endorsement in real-world applications.
REPRODUCIBILITY STATEMENT
We have taken several steps to ensure the reproducibility of our work. All datasets used our experiments are publicly available, with detailed descriptions provided in Appendix B. Model architectures, training procedures, and hyperparameters are documented in Appendix B.4 and Ap-
pendix B.5 The definitions and formulas of all faireess metrics included in Section and Appendix B.2
We also provide an anonymous code repository linked in the manuscript.
ACKNOWLEDGEMENTS
This work was funded in part by the National Science Foundation under award number IIS-2202699, 11S-2416995, IIS-2301599, 599, and CMM1-2301601
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APPENDIX
Related Work
19
A.1 Fairness notions
19
A.2 Approaches to mitigating unfairness
19
A.3 Fairness without harm
20
Fairness benchmarks
20
Experiment setup
21
Datasets details
21
2
Fairness metries
22
B.3
Methods
22
B.4
Implementation details
24
B,5
Hyperparameters
25
B.6
Prompts
25
Additional experiments
28
C.I
Training choice explorations
28
C.I.I
Impact of pretrained weights
28
C.1.2
Impact of optimizer choice fairness and utility
................................
28
C.1.3
Impact of batch size
31
C.1.4
Impact of weight decay
34
................................
C.1.5
Impact of model size
................................
34
C.1.6
Sensitity Analysis
................................
................................
34
C.1.7
Impact of model selections
37
C.2
Computational overhead comparison
................................
37
Large vision- languale model experiments
38
C.3.1
Model scale impact fairness
38
C.3.2
Prompt sensitivity LVLMs
38
C.3.3
Open-ended Generation with LVLMs
41
D
Adding new datasets and algorithms to the framework
42
Supplementary results
43
Limitations and future directions
44
Broader impacts
45
LLM usage
45
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A RELATED WORK
A.1 FAIRNESS NOTIONS
Various notions have been proposed in the ML literature to measure the unfairness of ML outcomes; they can be roughly classified into the following categories:
Unawareness: it prohibits the of sensitive attributes in the training and decision-making under the principle that excluding such features avoids direct discrimination (Dwork et al., 2012).
Parity-basee fairness: it requires certain statistical measures to be equalized across different groups (Zhang and Liu 202T /Zhang et al. 2020). Prominent examples include Demographic Parity (predicted positive rates should be similar actoss groups) (Dwork et al., 20122), Equal Opportunity (true positive rates are aligned) (Hardt et al. 2016), Equalized Odds (both false positive and false negative rates are aligned) (Hardt et al. 601 Predictive Parity (predictive value measures are balanced) (Chouldechova 2017D. Accuracy Parity (overall accuracy remains comparable across groups) (Khalili et al. [2033] Zhang et al. [219 Jin et al. 2026), etc.
inspired by the fair-division and envy-freeness literature in economics, it ensures that given the choice between various sets of decision outcomes, every group of users would collectively prefer its perceived outcomes, regardless the (dis)parity compared the other groups (Zatar et al. 2017 Ustun et al., 2019).
Preference-based fairness:
Counterfactual fairness: this notion leverages tools from causal inference and structural causal models to define fairness at the level of individual causal pathways. Intuitively, a model counter- factually fair if for any individual, the predicted outcome remains unchanged in a "counterfactual world" where the individual belonged to different demographic group, while all other non-sensitive attributes and causal mechanisms remain fixed (Kusner et al. 2017 Zuo et al. 2023).
Individual fairness: unlike other notions that ensure fairness at the group level, individual fairness attains fairness at the individual level, by ensuring similar individuals are treated similarly (Dwork et al. 2012).
Our work is mostly related to parity-based fairness. Unlike most existing methods that achieve fairness at the cost of reducing accuracy, we aim to achieve fairness without harm. By assigning group-specific models to different groups, our goal is to reduce the accuracy gap between different groups without sacrificing accuracy for any group (compared to the baseline classifier trained data collected from all groups).
A.2 APPROACHES TO MITIGATING UNFAIRNESS
A large body of research has focused on mitigating bias in ML models, which can be broadly categorized into three strategies: pre-processing, in-processing, and post-processing interventions.
Pre- Pre-processing methods aim to reduce unfairness at the data level. Common approaches include re- weighting or re-sampling training examples to balance demographic groups (Kamiran and
Calders 2012, Qraitem et al. 2023 Sagawa" et al., 2020 Pang et al. (024), generating synthetic data to fill representation gaps (Jang 20.1] or learning fair representations through data transformations (Celis et al.
and Mroueh, 2021
In- In-processing methods modify the training procedure by directly incorporating faimess constraints. Representative approaches include adversarial training, which encourages representations to be invariant sensitive attributes (Madras et al. 2018 Xu al. 2021 Tin et al.] 2024b) Pham et al. 2023), and fairness- based regularization terms in the loss function (Chuang and Mrouch 202 I Park et al.) 2022 Zafar et al. 2019). More recent work has leveraged contrastive learning (Shen
et al. 202 Park et al.) 2022 Wang et al. 2022) or disentangled representations (Creager et al.
2019 Park et
2021
Lee et al. 2021 to di-bias learned features.
Post-processing methods adjust model outputs to better satisfy faimnsss criteria without retraining (Khalili et al., b). Examples include modifying decision thresholds (Hardt et al. 2016). calibrating prediction scores (Pleiss et al.) 2017), or applying confidence-bast based and surrogate
adjustments (Menon and Williamson, 2018
Yin et al. 2024
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learning has gained increasing attention, especially due to the rising use of vision-and- -nd--annnddgggee models. Several approaches (Dehdashtian et al., 20244] Jung et al. 2024) have also been proposed to mitigate bias in mullii-modl settings. For example, FairCLIP (Luo et al., 2024) minimizes Sinkhorn distance between the two distributions to debias pre-trained vision-language models. (Seth et al. 2023) ensures fair representation from learning additive residual
Recently, faimess in
image representations. (Gerych et al. 2024) equalizes the distances between the debiased embeddings and images to achieve test-time fairesss for VLMs.
In this paper, we mainly employ pre-processing and in-processing methods (with one post-processing technique Kirichenko al. (2023). For clarity of presentation, we therefore re-group these strategies into two broader categories: data-centric methods, which intervene at the dataset or input level, and algorithmic methods, which modify the training process or outputs. This framing better reflects the scope of our study and emphasizes the practical trade-offs practitioners face when choosing between data-level and model-level interventions.
A.3 FAIRNESS WITHOUT HARM
Achieving fairness at the cost of lowering the performance of any group may be undesirable and even prohibited in certain applications. For example, in safety-critical domains such as healthcare, sacrificing model accuracy for fairness is undesirable, as it violates both the beneficence (i.e., doing what is best for patients) and non- non-maleficence (i.e., avoiding harm) principles in healthcare ethics (Beauchamp and Childress] 1994). Given this concern, some works aim to achieve fairness without negatively impacting model accuracy (Dutta al. 2020 Ustun et al.) 2019 Yin et al. 2024 Pang et al. 2024 Cai et al., 2025; Tan et al.) 2026). Instead of solely enforcing fairness constraints across different groups, these approaches ensure that model performance for every group does not deteriorate. For example, Martinez and Bertran (2019) seeks a Pareto-optimal fair ML model that minimizes performance gaps among groups while preventing unnecessary harm (i.e., minimal accuracy reduction for any group). Ustun et al. (2019) leverages individuals' sensitive attributes to train decoupled ML models, ensuring each group receives the best possible performance from its assigned model compared to a pooled model (trained on data from all groups) or the decoupled models of other groups. YYn et al. (2024) proposes a method using abstention, where pre-trained ML model selectively defers certain predictions to human decision-mate makers, thus achieving group fairness without reducing accuracy.
A.4 FAIRNESS BENCHMARKS
A number of toolkits have been developed to standardize fairness evaluation and mitigation. AI Faimess 360 (Bellamy et al. 2019) offers an extensible library of fairesss metrics and bias mitigation algorithms across datasets and tasks. Similarly, Fairlearn (Bird et al., 2020) provides practical tools for assessing and improving fairness in machine learning pipelines, with an emphasis adoption. Beyond toolkits, early benchmarking efforts such as Reddy et al. (2021) compared bias mitigation algorithms in representation learning, highlighting trade-off's across fairness metries. While these efforts established important foundations, they were largely limited to classical ML or relatively simple datasets and did not extend to complex vision or multi-modal contexts.
industry
Subsequent benchmarks have attempted to unify evaluation but remain limited in scope. MEDFAIR (Zong et al., 2023) targets fairness in medical datasets, addressing sensitive healthcare applications in vision tasks. FFB (Han et al. 2024) primarily evaluates older faimess algorithms before 2021 and omits recent advances representation learning and data-centric strategies, while also lacking systematic hyperparameter tuning. ABCFair (Defrance et al.] 2024) focuses on tabular datasets and adopts fixed hyperparameter settings, which restrict scalability and may misrepresent performance in more complex domains.
With the rise of large vision-languee models (LVLMs), newer benchmarks have begun to address multimodal fairness. Xia et al. (2024); : Jin et al. (2024a) investigate fairness in medical multimodal foundation models. GenderBias Xiao et al. (2025) and VLBiasBench Wang et al. (2024) explore bias in LVLMs but typically cover smaller models, leaving out the larger LVLMs increasingly deployed in real-world applications. VLAs (Girrach et al. (202)) is the most recent work, focusing specifically gender bias in LVLMS and corresponding mitigation strategies.
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In addition to benchmarking comparison faimess, recent research has also emphasized the importance of tailored analyses to determine appropriate mitigation strategies based on the specific nature of bias. For instance, Yang et al. (2023) focuses on addressing subpopulation shifts. Jonne et al. 2025) studied the fair representation learning method and found it not useful for performance- sensitive tasks. Furthermore, [Schrouff al. (2024) suggests considering the causal graph before performing data balancing for fairness. RRoschewitz et al. (2025) shows that identifying dataset shifts crucial for selecting the correct intervention, Matos al. (2025) studies the landscape of fairness metrics, highlighting the fragmentation in current definitions and the necessity of selecting metrics that align with real-world utility.
In contrast, NH-Fair is designed as a seneral-purpose faimess-withhou without-harm benchmark evaluat- ing multiple demographic attributes (e.g., gender, race, age) across diverse models, datasets, and mitigation strategies. Its distinct contributions are: (1) unifying evaluation across classical vision and multimodal models; (2) systematically analyzing the role of training settings; and (3) extend- ing coverage state-of-the-ard LVLMs at deployment-relevant scales, ranging from 4B to 109B parameters.
B EXPERIMENT SETUP
B. DATASETS DETAILS
Unless otherwise noted, we randomly split each dataset into training, validation, and testing sets with a ratio of 8:1:1.
CelebA (Liu et al., 2015): The CelebFaces Attributes Dataset, known as CelebA, is an extensive collection featuring over 200,000 images of celebrities, with each image annotated for 40 distinct attributes. In our research, we focus on the "wavy hair" attribute as the classification target while treating the "male" attribute as sensitive feature.
FaKeaae (Zhang et al., 2017): The UTKFaae dataset comprises more than 20,000 facial images, each labeled with information on age, gender, and ethnicity. For our analysis, we simplify the race attribute into two categories: "white" and "non-white as the sensitive attribute and take the "gender" as the classification target.
FairFace (Karkkainen and Joo, 2021): The FairFace dataset contains over 100,000 facial images annotated with age, gender, and race, emphasizing balanced demographic representation. We employ "race" prediction as the seven-class classification problem, and use "gender" as the sensitive attribute.
Facet (Gustafson et al. 2023); The Facet dataset includes facial images annotated with Fitzpatrick skin types, age, and gender. We utilize the binary attribute "visible face" as the classification target. Since a single data entry in Facet may have multiple skin type annotations, we use "gender" as our attribute for simplicity. Entries with incomplete gender annotations were removed.
HAM10000 (Maron et al. 2 The HAM10000 dataset a large collection of dermatoscopic images used for skin lesion classification. We reclassified its 7 diagnostic categories into two broad labels: "benign" and "malignant" following (Maron et al., 2019). Images with missing recorded sensitive attributes were excluded from the dataset. The HAM10000 dataset includes two sensitive attributes: age and sex. We binarized the age attribute into two categories: "young" and "old." Individuals aged 0-60 years were classified as "young," while those aged 60 years and above were classified as "old."
Fitzpatrick17k (Groh et al. 2021): The Fitzpatrick 17k dataset (Fitzl (k) comprises dermatological images labeled with Fitzpatrick skin types and diagnostic categories. We reclassify diagnoses into "benign" and "malignant" groups, following (Groh et al. 2021), I), and use skin type as the sensitive attribute, binarized into "lighter" (I-III) and "darker" (IV-VI). Images missing skin type diagnostic labels were excluded.
Waterbirds (Sagawa* et al. 2020): The Waterbirds dataset contains images of waterbirds and landbirds superimposed on either water or land backgrounds. We classify bird type (waterbird vs. landbird) the target, with background (water land) serving as the sensitive attribute. Instances with ambiguous background or species labels were removed. We use the train/ teann/alidation/test split provided with the dataset.
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Table Download URLs for the datasets.
Dataset
Download URL
License
CelebA Liu al. 2015
http:/ //mme//mmm.b...
Non-commercial research only
projectssCCleba. html
UTKFace Zhang et al.
2017)
hctps://ausance.
Non-commercial research only
github.1o/UTKFOS
FairFace Karkkainen and Joo
attp:://gtthb.co com/
CC BY 4.0
2021
joojs/fairface
Facer Gustafson et al. 2023
https://ai.no
Meta Images Research
com com/ con/datasetss/
facet 2acet-downloads
HAM10000
MMaron et al.
CC BY-NC 4.0
2019)
barvard.edu/dataat
xhtml
doi:10.7910//0
DBW86T
Fitzpatrick177 17k Groh et al.
https://githun
CC 4.0
2
com com/mattgroh/
fitzpatrick177
Waterbirds (Sagawa* al.
https://github. com/
No license specified
2020)
kohpangwel/group.Dn DRO
B.2
FAIRNESS METRICS
Overall Accuracy Parity (Gap): The accuracy/AUC gap between two sensitive groups.
Max-Min Fairness (Worst): The worst accuracy/AUC across sensitive groups.
Demographie Parity (DP): For binary classification, focus only the positive class. For multi-class classifications such as FairFace, we follow the fairness guarantees outlined (Denis
et al. 2024)
max [(((X) = y = a] P(((() = y = =a'] vEly
Equalized Odds (EqOdd): It is evaluated by ensuring parity in the probability of correct classifi- cation:
1 - [(x(X) Y = v. a P
Y = y. A = a']
We calculate this for all classes and take the average across classes as the final outcome.
B.3 METHODS
Data-centric methods
Rand (Cubuk et al. 2020): RandAugment (denoted as RandAug in the experiment results) commonly used in semi-supervised and unsupervised learning. It randomly selects and applies a set of data augmentations such as rotations, translations, brightness adjustments- to diversify the training data. In this study, we aim to evaluate whether training a model on more diverse data, without using demographic information,
lead to improved faimesss.
Mixup (Zhang et al., 2018): It is data augmentation technique that combines two training samples and their corresponding labels via linear interpolation to create new synthetic examples. This encourages the model to learn smoother decision boundaries and reduces overconfidence on specific groupecorrelated features, forcing the model to generalize beyond rigid group distinctions. We apply Mixup by blending data from different groups evaluate impact on fairness and performance.
Mixup (Zhang et al., 2018): This data augmentation technique combines two training samples and their corresponding labels via linear interpolation to create new synthetic examples. We apply Mixup by blending data from different demographic groups; this encourages the model to
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learn smoother decision boundaries and reduces overconfidence on specific group-correlated features, effectively forcing the model to generalize beyond rigid group distinctions.
Resampling (Buda et al. 2018; Sagawa* et al. 2020): It balances the dataset distribution by either over-sampling underrepresented groups or under-sampling overrepresented groups. This is done by assigning weights to each sample, helping to address class group imbalances.
Bias Mimicking (Qraitem et al., 2023): It introduces a caas-coondiionee sampling technique that breaks the correlation between labels and known attributes. It constructs a subsampled distribution to mimic a biased distribution for all classes during training, thereby statistically minimizing the correlation between sensitive attributes and targets.
FIS (Pang et al., 2024): Fair Influential Sampling (FIS) mitigates group disparities without using sensitive attributes during training. It assumes group labels are available only for the validation set and scores each candidate by its estimated influence on both accuracy and a faimess objective (e.g., risk disparity) evaluated on that validation set. The algorithm then actively adds the influenncee examples to the training data, inducing a targeted distribution shift that reduces disparity without harm.
A Algorithmic methods
Decoupled Classifier (Ustun et al., 2019 Wang al., 2020): It trains separate classifiers for each sensitive group, allowing group-tailored decision boundaries and then aggregates their predictions.
LAFTR (Madras et al. 2018): Learning Adversarially Fair and Transferable Representations (LAFTR) trains a feature extractor (representation function) so that the learned representation is predictive of the target while being uninformative about the sensitive attribute. Concretely, predictor is optimized to minimize task loss while an adversary is trained to predict sensitive at- tribute to enforce conditional independence. Since the constraint is imposed at the representation level, the learned feature extractor supports downstream fair prediction.
FSCL (Park et al. 2022): ; Fair Supervised Contrastive Loss (FSCL) integrates faireess consider- ations into a contrastive loss function. leverages contrastive learning to separate representations from different classes and align representations of the same class from different groups to achieve
fair classification.
GapReg (Chuang and Mroueh| 2021): Gap Regularization embeds chosen group-fairness greuty metric, such as Demographic Parity, Equal Opportunity, and Equalized Odds, directly into the training loss function to balance both accuracy and faimesss.
MCDP (Jin et al., 2024b): It introduces a fairness metric that captures the maximal local disparity by evaluating cumulative ratio disparities across varying neighborhoods. MCDP uses a regularization approach similar to GapReg by adding the regularization term to the task loss function during training to optimize the model.
-GrouDDRO (Sagawa* et al. 2020): GroupDRO focuses on protecting the worst-case groups. It maintains per-group weights that are increased for groups with higher loss and forces the model to improve the worst-off subgroup rather than overfitting to the majority, thereby improving fairness for minorities.
DFR (Kirichenko et al., 2023) Deep Feature Reweighting (DFR) is a prst-proesssing method. It freezes the feature extractor of a standard ERM model and retrains only the final linear layer (the classifier) on small reweighted set where the spurious correlation is broken. Following the original setup, we use a group-balanced reweighting set (i.e., a balanced distribution across sensitive groups) so last-layer retraining emphasizes core features over spurious cues. DFR provides a lightweight and efficient approach for achieving better predictions.
-xonfair (Delaney et al. 2024): Oxonfair is an open source toolkit for enforcing fairness in binary classification through post-procsssing methods. supports multiple fairness notions and is able to minimize degradation while enforcing fairness. Since it only supports binary classification, we omit the FairFace dataset (the multi-class classification task) for this method. We follow their official implementation that enforces fairness validation data and test on the test data. We consider five different combinations of optimization target and fairness constraints based on our benchmark tasks and metrics. These combinations can be found in Table [ For datasets with AUC metrics, we change the accuracy optimization objective to balanced accuracy.
Multi-modal models
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CLIP (Radford et al,, 2021): CLIP (Contrastive Srrguage-Imagee Pretraining) learns visual concepts from natural language supervision by jointly training on image-text pairs. In this paper, we use the VIT (Dosovitskiy al. 202T as the backbone. In addition, consider two CLIP- based post-training debias methods: FairerCLIIP (Dehdashtian et al. 2024) and SFID (Jung et al. 2024). FairerCLIP mitigates bias by projecting image and text representations into Reproducing Kernel Hilbert Space (RKHS) and minimizing the Hilbert-Schhe Schhitt Independence Criterion (HSIC), thereby statistically enforcing independence between the embeddings and sensitive attributes. SFID (Unified Selective Feature Imputation for Debiasing) identifies features reliant on spurious correlations and applies a selective imputation strategy reconstruct representations that are invariant to demographic shifts.
BLIP2 (Li et al., 2023): BLIP-2 bridges frozen vision encoders and frozen LLMs using a lightweight Q-Former trained in two stages to extract language-aligned visual tokens. We use BLIP2 to generate the image embeddings and text embeddings and calculate the embedding similarities to make classifications.
LLaVA-1.6 (Liu et al., 2024): LLaVA (Large Language and Vision Assistant) is an auto- regressive language model that aligns vision features with language model using instruction tuning.
Qwen2.5-VL (Bai al. 2025): Qwen2.5-VL is the latest model of Qwen vision-language series from Alibaba. It integrates a vision encoder and a language model decoder to process multimodal inputs and achieve comparable performance with GPT-4o and Claude 3.5 Sonnet.
Gemma 3 (Tea et al., 2025): Gemma is family of lightweight open models released by Google, ranging from TB to 27B parameters. As the 1B model supports only text inputs, we evaluate the multimodal capabilities of the 4B, 12B, and 27B variants in this paper.
-lamaa 3.2 Grattafiori et al., 2024) and Llama (Meta) 2025): Llama is the latest multi- modal model released by Meta. Due to the computation resource limitation, we only evaluate Llama Scout, a smaller variant compared with Llama Maverick, alongside the Llama-3.2 multimodal series.
We present all used models and their sources in Table All data-centric and algorithmic methods except RandAugment sensitive attributes during training, while the decoupled classifier requires access to the sensitive attribute at deployment.
B.4 IMPLEMENTATION DETAILS
The experiments were conducted on a supercomputer cluster, where each node is equipped with two AMD EPYC 7643 processors, four NVIDIA A100 GPUs (80GB memory each), and 921 of RAM. The code is implemented with Python 3.10.12 12 and PyTorch 2.5.0. All images are resized to 224 x 224 pixels, and during training, we apply random horizontal flipping for data augmentation. We use a pre-trained ResNet-18 as the backbone initialization to start the training. In addition, we conduct experiments on pretrained weights and model size in Appendix C
For RandAug, color-based augmentations were excluded during training, as the sensitive attributes in our study include skin type, and such augmentations could inadvertently alter needed features.
The Fairness Influence Selection (FIS) method was originally designed for an active learning setting, where a portion of the dataset remains unlabeled. In its original formulation, FIS selects necessary unlabeled data using combination of pseudo-labele and ground-trute labels. To adapt it to our setting, we employ ground-truit labels for influence-guided selection. Specifically, the training set is randomly partitioned into two subsets according to predefined ratio: one subset is used for supervised training, while during validation, the model assesses the influence of data in the second subset and selectively incorporates subset of these samples into the training set to achieve fairness without harm. The split ratio is treated as a hyperparameter, as varies across different datasets.
For Seirness-Sensitive Contrastive Learning (FSCL), training divided into two phases: The first 60 epochs perform contrastive learning. The subsequent 40 epochs were used for classifier training.
For GapReg and MCDP, the differential privacy (DP) loss function was extended to accommodate multi-class classification, particularly for the FairFace dataset. This extension is based on the multi-class demographic parity formulation proposed by (Denis al. 2024).
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Table 6: Summary of multi-modal models
Model
#Param
URL
CLIP
CLIP-VIT-B/16
150M
httpss//gitttub.omm/pees//////
BLIP-2
BLIP-2 Base
IB
sttps///gthha..cc//aallsffoetoe
LAVIS
htpps//buggiaggacccc////aa22111
LLaVA.vl.6-vicuma-llbb
7B
LLOVA-v1..6-v.ccnaa----111
https//huggingffcccc/////
LLaVA
LLaVA-w1.6vvicun-IBbb.
13B
LlaV--v..6-vicua--1333111
Ltw.:aaccc....oLa--aaaaaa atps:/huugingiacc.com LLaVA-ht/
LLaVA-v1.6-34b-hf
34B
ILVVA-V2.6-34b-11
https://hucg/iggfan
/wee//
Owen2.5-V1-77-Instme
7B
-wen2.5-VL--B--nsee
brtps://uuggiggaace
Owen/
Qwen
Swwe2..5-1-332B-nnstt
32B
2wen2.5-74-33--Iaazaae
Qwen/
Owen2.5-VV-72BB-nsstruc
72B
bttps///hccchnffaaecccoooo
gemma-3-4b-it
4B
gemma-3-4b-17
Gemma
gemma-3-12b-it
12B
gemma-3-12b-1
https://hieg
gemma-3-270-n
27B
1-mma-3-27b-
https:
Llama-3.2-1IB 3.2-11B-Vision- -istent
11B
co/ met -at.ttt-lllaaa111
ama-3
2- 18 vi11--Visionnnsttre
Llama
Llama-3.2-90 Vision-Insttuctt
90B
co Loometa--laam/LLl
2-90B-Vision Vii--iiiioo---stzue
Llama Scout (17Bx16E)
204-Scout-17B-166-
For CLIP and BLIP2, we use their public implementations. For LLVMs, we adopt the open-source weights from Huggingface for all models with BF16 or FP16 precision, depending on their suggested model loading.
B.5 HYPERPARAMETERS
We perform extensive hyperparameter optimization using Bayesian hyperparameter search via Weights & Biases on the validation results. The default batch size is set to 256 for all experiments. For the SGD optimizer, we deploy StepLR scheduler and set momentum to 0.9, where the learning rate reduced by factor of 0.1 every 30 epochs. The number of hyperparameter search iterations varies with the complexity of each method-datase combination, subject to tuning budget of 100-200 searches per method-daten ranges, as detailed in Table 7 24 denotes a uniform distribution, and log LA denotes a log-uniform distribution. The benchmarking process required total of approximately 1.1 GPU years. All methods were trained for 100 epochs with early stopping to prevent overfitting. Early stopping is used if the validation loss or validation accuracy/AUC does not improve after 10 epochs.
pair. The search space includes both discrete choices and continuous
B.6 PROMPTS
Table 8 lists the zero-shot prompts used throughout study. For image- text matching models (CLIP, BLIP-2, FairerCLIP, SFID), we use one template sentence per class and compute the im- age-caption similarity, then pick the label of the highest-scoring caption as the prediction. For large
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Table 7: Hyperparameter search space for all methods.
Category
Hy Hyperparameterr
Search Spuce
Leurning Rate
log24(10 4, 10 2) (0, 10 10-5, 10-2, 10-2) [Adam, SGD)
Common Hyperparameteers
Weight Decay Optimizer
Mixup
Mixup loss coeflicient
(4011, 10)
Resampling
Resampling
(Group, Group Class)
BM
Sampling mode
{none, os)
Lahel ratio
[0.1, 0.3, 0.5)
FIS
Fairness metric
(dp. cup. eod]
Class coefficient
24(0.1, 1(0.1.1)
LAFTR
Fairesss cocfficient
FSCL
Group notmalization
{0, 1]
Faimess regularization objective Regularization coefficient
(dp, cop, cod)
GapReg
((0001,5) 5)
Regularization coefficient Temperature
(4(0001 5) {5. 10. 50, 100)
MCDP
GroupDRO
Alpha
1((0.01.5) (0.001,
Gamma
Tune class weights
(0. I] 1]
Add train Regularization
DFR
(LI, L2)
optioos
[3. I, 0.7, 0.7, 0.1, 0.07, 0.03, 0.01, 0.003)
0.3,
Class weight options
(1. 3. 10, 100, 300. 1000)
faccuracy. accuracy.diff optimize accuracy
reducing gap
jaccuracy.min, accuracy) # optimize worst without reducing overall (accuracy.dis diff, accuracy.min) optimize gap without reducing wonst (oqualized odds, accuracy) # optimize EqOdd without reducing overall [demographic, www.itt.pppt......oe accuracy) optimize DP without reducing overall
Oxonfair
Objective Construint
24(0) 1)
Te.
24(0,1
FLi
14(0
Ta.t
1((01)
RFF Dim
(1000, 2000, 3000, 4000, 8000)
threshold
2/(0.5,
SFID
Number of Pruned Feature
(50,75,100)
vision-language models, we feed the prompt with image input to the model and then parse the model's short text answer.
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Table 8: Prompts used datasets for text-image matching and visual-language model (image-
text-to-text) inference.
Dataset
Prompts
Image- Text Matching:
["A photo of a person with non-wavy hair",
"A photo of a person with wavy hair.]
CelebA
Image-Tess Text-to- Text:
Is the person in the photo wavy- aavy-haired?
Only Answer 'Yes" for wavy hair, 'No' for non-wavy hair.
Image-Tese Text Matching:
["A photo of male.".
"A photo of a female.") "]
FTCeFace
Image-Texxe Text-to-Text:
Is the person in the photo male or female?
Only Answer 'Male' or 'Female'.
Image- Text Matching:
["A photo
White person.".
"A photo of a Black person.".
"A photo of a Latino or Hispanic person.",
"A photo of an East Asian person.",
"A photo of a Southeast Asian person.".
FairFace
"A photo of an Indian person.".
"A photo of a Middle Eastern person."]
Image- Text-to- Image-Text-to- Text:
Based on the photo, classify the race the person into:
0: White. 1: Black, 2: Latino or Hispanic, 3: East Asian,
4: Southeast Asian, 5: Indian, 6: Middle Eastern.
Please answer with the corresponding category number only.
Image-Text Matching:
"A photo of a person with non-visible face."
"A photo of a person with visible face."
Facet
Image- Teagg--ext-tt--le
Does the photo show visible face?
Only answer YYes' if the face is visible, otherwise answer 'No'.
Image-Tex: Text Matching:
"A photo of a landbird."
"A photo of a waterbird."
Waterbirds
Image-Tee Text-to-Tee Text:
Is the bird in the photo landbird waterbird?
Only answer 'Landbird' or 'Waterbird'.
Image-Tess Text Matching:
HAM10000/ Fitz17k
photo of a benign skin condition."
"A photo of a malignant skin condition."
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C ADDITIONAL EXPERIMENTS
C.I TRAINING CHOICE EXPLORATIONS
In this paper, we argue that carefully tuned ERM often rivals specialized debiasing methods. We then organize the further studies to mirror the life cycle of a standard vision pipeline. We start with initialization choices from pre-trained weights versus training from scratch, because the features model begins with strongly constrain downstream bias. Next, we study optimizer choice to challenge prior work that often fixes optimizers across different settings and evaluate their influence on both utility and fairness. We then turn to other common training choices like batch size, weight decay, and model/checkpoint selection. Together, these investigations shed light on faimess outcomes and also help reduce the search space for future hyperparameter optimization, providing principled guidance on which training choices matter most.
C.1.1 IMPACT OF PRETRAINED WEIGHTS
Loading pretrained weights improves overall utility without harming fairness. In this part, investigate the impact of loading pretrained weights on ImageNet (Deng et al. 2009) before training on fairesss and utility ERM. For clarity, F extraacs the most representative panels from the full grid in Figure 7 Each panel plots models trained with pretraining (red circles, "Yes") and from scratch (blue circles, "No") across our seven datasets while sweeping the same hyperparameter grid, making the influence of weight initialization easy to see at a glance. The horizontal axis of each plot measures subgroup performance disparity (e.g.. "Gap AUC" or "Gap ACC"), while the vertical axis measures overall utility (e.g., "Overall AUC or "Overall ACC"). In all cases, points closer to the top-left corner indicate both higher overall performance and smaller subgroup gaps, which represent more favorable fairness-utility trade-offs. A general trend emerges showing that models initialized with pretrained weights tend to achieve higher overall performance than those trained from scratch. This benefit is especially apparent in datasets with relatively small training sets, such as 1k and Waterbirds. However, pretrained models do not consistently outperform models trained from scratch in closing the subgroup performance gap. This observation is further supported by other fairness metrics (such as DP, EqOdd, and Worst) reported in the Figure 7 where the full comparison is provided. Despite this mixed impact fairnsss, pretrained models improve overall utility without harming any particular subgroup, particularly benefiting disadvantaged groups (as seen in the Worst metric in the appendix). This aligns with our "fairness without harm" principle. As a result, we adopt pretrained models in our benchmark for their superior overall performance while maintaining acceptable fairness levels.
C.1.2 IMPACT OF OPTIMIZER CHOICE ON FAIRNESS AND UTILITY
Using fixed optimizer across methods and datasets can lead to unfair comparisons. Many prior studies use a single optimizer across datasets and methods, sometimes even without fine-tuning learning rates. Based on this, we conducted this study two baseline methods and two datasets to provide empirical evidence that using a fixed optimizer across methods and datasets can lead to unfair comparisons. Our findings in Figure g optimizers, and selecting the right one can improve both utility and fairness. For instance, models trained on CelebA with SGD tend achieve better utility and smaller accuracy gaps, resulting in a more balanced model compared to those trained with Adam. This is evident in the results, where SGD-based models cluster around higher accuracy values with lower fairness gaps. On the other hand, in the Fitz f 7k dataset, models trained with Adam perform better, achieving higher utility while maintaining competitive fairness scores. This variation in optimizer performance highlights that a fie-siz--itt-all approach can lead to suboptimal results, especially in fairness-sensine settings. Our findings underscore the need for more equitable and transparent evaluation practices in future fairness research.
indicate that different datasets respond differently to various
Further Analysis (ERM Focus). In the main text, due to computational constraints, we primarily experimented with SGD (Bottou 2012) and Adam (Kingma and Ba 2015). Here, we extend the study to include two additional optimizers, AdamW (Loshchilov and Hutter 2019) and Adagrad (Ward et al. 2020). in order to better understand their impact ERM performance. Across datasets, optimizer choice exhibits a more pronounced and consistent influence on both utility and fairness than model size batch size. For example, Waterbirds, Adam W and Adagrad achieve both higher
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60-
Overall
60-
Overall Acc
m0
Overall Acc
75-
10-
No
No
No
70-
165
15
Gap
Gap
(a) CelebA
(b) UTKFace
(c) FairFace
90-
30-
Overall AUC AuC
85
Overell AUC
Overall
65-
Yes
15
10
Gap Acc
Gap AJC
Gap AUC
(d) Facet
(e) HAM10000
(f) Fitzl7k
Figure 6: Comparison of utility gap for the pretrained weights. More left indicates greater fairness (smaller accuracy gap), while higher values indicate better performance.
Table 9: Performance comparison across different optimizers.
Dataset
Optimizer
Utility (Acc or AUC)
Worst
Gap
EqOdd
DP
SGD
86.57 = 0.18
83. + 0.23
6.76 = 0.34
81.91 + 0.58
67.20 = 0.69
Adam
86.11 1 0.43
82.85 1 0.61
7.89 1 0.29
80.46 1 3:23
67.22 1 2.81
AdamW
86.48 + 0.14
83.30 = 0.19
7.67
: 0.12
79:27
+ 2.36
66.03 + 1.88
CelebA
Adagrad
86.65 + 0.14
83.62 + 0.25
7.33 +: 0.27
81.39 + 0.88
67.90
0.32
Std
0.21
0.35
0.43
1,00
0.67
SGD
91.95 1 0.03
90.51 0.22
3.30 0.55
96.76 1 0.58
95.56 1: 0.34
Adam
92.75 : 0.54
91.78 + 0.61
2.26 = 0.64
97.62 - 0.53
94.55 = 1.20
AdamW W
92.85 1 0.28
91.67 1 0.37
2.72 1 0.71
97.30 1: 0.70
95.70 70 1 0.21
UTKFaae
Adagrad
92.84 1 0.22
91.27 1 0.28
3.59 1 0.16
96.45 1 0.15
95.17 1 0.41
Std
0.38
0.50
0.51
0.46
0.45
SGD
65.68 = 0.39
64.64 = 0.34
186 0.31
95.98 = 0.42
97.36 = 0.21
Adam
66.76 0.21
66.34 0.37
0.87 1
96.22 1 0.57
97.61 0.41
AdamW
67.09 1 0.39
66.79 1 0.42
0.64 1 0.06
96.50 1 0.38
97.69 1 0.17
FairFace
Adagrad
67.54 + 0.43
66.83 + 0.73
1.50 + 0.64
96.07 + 0.41
97.65 + 0.17
Sid
0.69
0.89
0.49
0.20
0.13
SGD
67.53 = 0.37
64.25 4 0.97
4:31 = 1.13
96.47 # 1.23
95.40 1.12
Adam
66.34 + 0.89
63.09 + 1.33
4.23 - 0.87
94.60 + 1.40
94.17 = 1.23
AdamW
67.46 = 0.17
62.23 = 0.74
6.80 = 1.14
95.67
+ 1.22
94.62 = 0.88
Facet
Adagrad
67.62 0.13
0.23
6.02 1 0.31
97.15 1 0.91
95.95 0.79
Std
0.52
0.72
1.11
0.95
0.69
SGD
88.35 +: 1.83
84.68 4: 2.02
4.112.08
18.17
82.22 1 4.78
Adam
86.47 = 1.13
81.74 = 1.72
4.53 = 1.66
92.55 + 2.43
87. 67.69- + 2.69
AdamW
88.18 1 0.66
84.38 1 0.74
3.40 1.13
89.02 1 6.00
80.23 1 7.57
HAM10000
Adagrad
87.00 + 0.90
81.98 2.86
5.27 3.117
91.33 4.661
85.52 + 1.04
Std
0.79
1.34
0.68
1.75
2.88
SGD
89.74 = 1.00
88.39 = 1.05
2.92 2.92 1.14
94.92 2.48
94.46 = 1.40
Adam
89.62 + 0.53
87.91 0.47
2.34 0.41
94.90
0.92
91.74 0.82
Fitz17k
AdamW
91.37 + 0.26
90.45 - 0.32
2.11 0.91
93.91 + 1.59
92.32
0.62
Adagrad
91.43 + 0.27
90.40 = 0.56
3.51 = 1.66
96.09 + 0.95
93.40 = 0.52
Sid
0.86
1.15
0.54
0.77
1.04
SGD
86.45 + 0.93
83.72 + 1.68
3.57 + 1.82
08.23 + 1.97
76.09 + 2.03
Adam
85.63 + 1.36
84.20 + 0.94
2.87 + 0.85
66.53 3.31
77.67 + 3.52
AdamW
87.09 - 0.54
86.25 - 0.26
1.68
- 0.90
71.90
81.54 0.80
Waterbirds
Adagrad
87.77 1 0.64
87. 1 0.86
1.35 1 1.54
74.00 1 3.04
81.35 + 2.02
Std
0.98
1.40
0.90
2.94
2.35
overall accuracy and smaller subgroup gaps compared to SGD and Adam, indicating a more favorable hirness-utiliin trade-off. These results suggest that the relative advantages of each optimizer
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CelebA
Celebd
Celebd
Conret ACE
Coerat Arct =
Opent
76-
78
Gay
WertArr
Foods
Dp
LTTiPace
UTKewce
Jice
Coeer boce
10-
WorstAtc
Ezods
Fairface
Pain face
Overall ACe
Overn
350
Worts Acc
Ecods
Dvvest Act
Comnil
Bc
Weet
Oueek AUC
Overal AUC
64
15
72-
Fiz17k
Rtz17k
Riz17k
offectto
ND
Nc
10
76
025
10.
525
260
915
1066
Visterbirds
Watortinds
Visterbincs
Wisterbinds
Oveest
Yoa
Yee
15
26
05
46
66
164
20
C6
100
Figure 7: Full comparison of fairness metrics (Gap, Worst, EqOdd, DP) across all datasets for the pretrained weights.
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56
Overall
Adom
Adam
SGD
3.5
5.0
10
Gap
(a) GapReg CelebA
(b) GroupDRO CelebA
92.5
90
Overal AUC
07.3
85.0
80-
80.0
Adam
77.5
SGD
15-
SGD
Gap AUC
Gop AUC
(c) GapReg on Fitz17k
(d) GroupDRO on Fitz17k
Figure 8: The effect of optimizer choice on fairness and utility trade-offs across CelebA and Fitz 7k. The points from the same color are models trained with different learning rates and weight decays.
highly dataset-dependent, yet their effects utility and faimess are clearer than those of other hyperparameters. Thus, when conducting hyperparameter optimizations, the choice of optimizer could be prioritized for fairness- evaluations.
C.1.3 IMPACT OF BATCH SIZE
Batch size has limited effect overall utility but affect fairness slightly. In this benchmark, we use a batch size of 256 for all methods. We also evaluate batch size have an influence on model fairness. We conduct experiments on seven datasets with different batch sizes ranging from 32 to 1024. The full results are reported in Table and visualized in Figure 9 Across all datasets, utility remains relatively stable as batch size increases. However, worst-case performance and the accuracy gap fluctuate more significantly. In some cases, such as CelebA and HAM10000, worst-case performance tends decrease slightly with larger batch sizes. This indicates that larger batch sizes might lead to increased disparities between advantaged and disadvantaged groups. Equalized Odds and Demographic Parity vary highly depending on specific datasets. For datasets like CelebA and Waterbirds, EqOdd tends to decrease batch size increases, while EqOdd and DP increase with larger batch sizes in HAM10000 and Fitz17k. The results show that batch size can influence fairness, but not in consistent way. However, since its influence fairness is relatively minor and inconsistent, batch size can be given lower priority future hyperparameter searches.
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CalebA
Fairface HAMI0000
Waterbirds
CelebA
Fairface
HAM100000
Waterbirds
95
95
Fitz7k
85
Utility
80
80
70
70
60
60
32
128
256
512
1024
64
128
256
512
1024
Batch Size (log scale)
Batch Size (log scale)
100
1.0
UTKFace
Facet
-Fitz17k
90
Gap
80
75
66
CelebA
Fairface Facet
HAM10000
Waterbieds
UTKFaco
Htz17k
32
25
512
1024
64
128
256
512
1024
Batch Size (log scale)
Batch Size (log scale)
95
85
75
CelebA
FairFace
HAM10000
Waterbirds
UTKFace
Facet
Fit27k
60
512
Batch Size (log scale)
Figure 9: Effect batch size on all metrics across seven datasets derived from Table 10 Each subplot corresponds one evaluation metric, and curves represent different datasets. The x-axis is plotted on a log, scale for clarity while keeping tick labels in the original batch-size units.
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Table 10: Performance comparison across different batch sizes.
Dataset
Batch Size
Utility (Ace or AUC)
Worst
Gap
EqOdd
DP
32
86.48 1 0.25
83.59 1 0.35
6.94 1: 0.43
80.82 1 1.56
66.77 1 1.19
86.63 0.20
83.79 0.35
6.82 + 0.50
81.72 1.86
67.37 + 1.27
128
0.12
83.69 0.23
6.73 0.59
81.80
0.87
67.23 1.14
CelebA
256
86.57 = 0.18
83.76 0.23
6.76 + 0.34
81.91 0.58
67.20
512
86.50 = 0.12
83.60 + 0.21
6.96 + 0.35
81.57 = 0.93
67.09 = 0.76
1024
86.52 +: 0.09
83.67 + 0.14
6.86 +: 0.32
81.60 1: 0.64
67.28 + 0.66
Std
0.05
0.07
0.09
0.35
0.19
32
93.07 1 0.47
92.15 0.68
2.12 1 0.98
97.37 0.42
94.21 1 1.66
64
93.28 + 0.18
02.53
1.74
98.22 0.55
94.80 = 1.98
128
92.94 +: 0.50
91.72
2.84
97.16.00.63
94.73 + 1.23
UTKFace
92.75 0.54
91.78
2.26100.6
1
94.55 1.20
512
93.30 0.39
0.78
1.89
98.10
95.84 1.17
1024
92.80 + 0.54
91.62
2.75+00.81
0.82 97.25
95.13 + 1.88
Std
0.21
0,36
0.41
0,41
0.51
32
67.49 1. 0.33
66.64
1.80
1.01
96.39
U.43
97.80 80 1: 0.31
64
67.75 0.50
6..53
0.67
0.49
96.13 0.44 0.44
97.24 = 0.27
128
66.76 1: 0.54
0.73
0.76
96.32 0.66
97.56 + 0.40
FairFace
66.76 0.21
0..3
0.87
96.22 1
97.61 1 0.41
512
66.63 + 0.39
0.56
1.54
96.15 0.58
97.59 0.31
1024
65.77 + 0.65 0.64
0.75
1,23 1.23+
96.05 + 0.46
97.41 + 0.34
Std
0.68
0.40
0.12
0.17
32
67.55 1 0.27
64.11
4,50
96.33 1: 0.93
95.31 1 0.87
64
67.55 + 0.48
61.23
4.35 1.15
97.11 1.05
96.18 = 0.99
128
67.68 +: 0.14
63.61
5.31 1.02
5.31
97.08 1: 0.74
96.05 1 0.72
Facet
67.55 0.37
64.25 0.97
1.13
96.47 1
1 1.12
512
67.58 0.11
63.85
4.88 1.07
97.31
97.14 0.94
1024
67.08 + 0.35
5.72 1.16
98.11 + 1.06
97.87 + 1.18
Std
0.19
0.53
0.52
0,58
0,92
32
89.35 1: 2.18
15.99:3.770
4.30 2.09
0,0413363
82,25 1 4.28
64
89.32 + 1.44
06.08-3.08
3.79 4.9943..4
88.25 + 4.44
82.91 + 3.45
89.21 + 1.73
84.70 3.60
5.82 2.99
86.80 4.09
$1.80
0.89
HAM10000
1.83
2.02
4.11 1 2.08
88.17
512
88.21 1.69
83.61 2.71
5.08 1 2.79
89.07
84.73
3.34
1024
88.17 + 1.26
83.30 +: 2.27
5.40 +: 2.86
87.22 1: 5.60
84.91 1 2.35
Std
0.53
0.96
0.73
0.80
1.23
32
90.62 1: 0.82
89.45 =: 0.83
2,80 1: 1.34
96.50 1 2.73
94.55 1 1.12
64
90.36 + 0.67
89.02 + 1.34
2.73 1.58
96.13 + 2.08
94.85 = 1.00
89.92 + 0.66
88.50 0.80
3.16 1.25
95.51 + 2.53
94.54 1.22
Fitz17k
89.74 1 1.00
88.30
2.92 1 1.14
94,92 1 2.48
94.46 1 1.40
512
88.94 + 1.15
87.70 1.18
2.61 1.27
95.65 2.36
95.27 1.20
1024
86.97 1: 1.38
1
2.82 1: 1.56
95.95 +: 1.05
96.49 1 0.84
Std
1.22
1.28
0.17
0.50
0.71
32
87.20 1: 1.25
85.81
2,98 0.88
73.88 1: 3.14
82.33 1: 2.95
64
87.70 + 0.61
4
2.68 1.45
74.60 + 1.83
83.17 - 1.81
88.14 0.62
87.02
2.24
75.69 - 1.96
84.11 + 1.70
Waterbirds
1.36
84,20
2.87.0.88.
66.53 3.31
77.67 1 3.52
512
87.11 + 1.30
86.03
2.16 1.38
72.69
3.75
81.45 2.89
1024
83.68 1: 1.19
81.35 1 2.41
4.65 1 2.78
63.98 1: 1.60
73.77 1 3.19
Std
1.52
1.89
0.83
4.38
3.60
33

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Published as a conference paper at ICLR 2026
C.1.4 IMPACT OF WEIGHT DECAY
Weight decay is critical hyperparameter that affects both model generalization and utility. The results in Table [1 suggest that while weight decay influences fairnsss, its effects vary significantly across different datasets. L2 regularization tends to maintain or slightly improve faimess metrics, whereas LI regularization can, in some cases, amplify disparities, particularly in datasets with imbalanced subgroup distributions. Since regularization typically the default and widely adopted choice in hyperparameter optimization pipelines, while I.1 regularization is used less frequently, we additionally include comparison between L2 regularization and no regularization (AL2-No) in the table. This expanded setup provides clearer view of the marginal effects of L2 regularization observe that tuned L.2 weight decay rates have only minor effects (less than 0.5% in most cases) on fairmess metries for most datasets, suggesting that smaller tuning budget could be allocated to this hyperparameter.
fairness. We
Table 11: Performance comparison across different types of weight decay.
Dataset
Method
Utility (Acc AUC)
Worst
Gap
EqOdd
DP
86.52
83.68 0.26
6.82
0.42
81.48
67.10 I 1.03
CelebA
86.57 0.18
83.76 0.23
6.76
0.34
81.91 0.58
67.20 0.69
No
86.52 0.20
83.65 + 0.34
6.91 + 0.43
1.37 82.15
67.69 + 1.06
A12-No
0.05
0.11
-0.15
-0.24
-0.49
L1
93.02 + 0.34
91.85 = 0.54
2.711122
97.21 = 1.12
95.13 = 1.23
UTKFace
L2
92.75 + 0.54
91.78 = 0.61
2.26 1 0.64
97.62 0.53
94.55 + 1.20
No
93.11 + 0.49 -0,36
91.86 + 0.85
2.92 + 1.01
97.22 + 0.97
94.69 + 1.26
A 12--No
-0.08
-0.66
0.40
-0.14
L1
66.60 = 0.16
66.07 = 0.26
1.13
96.20 0.37
97.61 67.61 = 0.25
FairFace
L2
66.76 0,21
46.34:0.:3 0.37
0.87 1 0.51
96 22 - 0.57
97 61.6 + 0.41
67.01 0.17
00.71
0.63 0.54 0.54
95.99 + 0.52
97.47 + 0.42
Ar2- No
-0.25
-0.37
0.24
0.14
67.85 0.19
63.71
5.41
96 0.85 96.47
96.12 I 0.68
Facet
67.55 0.37
64.25 0.97
4.31 + 1.13
95.40 + 1.12
67.60 0.40
64.24 0.64
4.39 = 0.87
96.26 1.09
95.39 + 1.02
A12-No
-0.05
0.01
-0.08
0.21
0.01
LI
87,66 1.11
83.70 3.05
3.86 1 3.46
87.75 1 4.03
83.95 I 5.10
HAM10000
1.2
88.35 + 1.83
84,68 + 2.02
4.11 + 2.08
88.17 3.10
82.22 + 4.78
No
88.12 + 1.89
84.85 185::20 2.003
3.42 + 1.54
88. + 4.13
83.78 + 3.87
A22-No
0.23
-0.17
0.69
0.03
-1.56
L1
82.26 + 1.68
79.79 = 2.52
4.70 = 2.39
97.10 = 1.45
97.91 + 0.45
Fitz17k
L2
89 74 + 1.1.1.00 1.00
88.39 + 1.05
2.92 1.14
94.92 +: 2,48
94.46 + 1.40
No
89.65 0.96
88.30 + 1.03
2.94 + 1.10
2.80 95.08
94.36 + 1.31
Ar2-Na
0.09
0.09
-0.02
-0.16
0.10
77.46 0.64
76.79 = 1.09
1.33 = 0.93
88.83
2.35
91.68 1.99
Waterbirds
85. 1.36
84.20 1 0.94
0.85
3.31
77.67 1 3.52
No
85.38 0.68
83.38 = 1.58
4.00
69.10 1.04
76.53 + 1.56
A12-No
0.25
0.82
-1.13
-2.57
1.14
C.1.5 IMPACT OF MODEL SIZE
Increasing model size does not consistently improve fairness. We analyze the effect of model size on fairness by comparing different ResNet architectures (ResNet-18, ResNet-34, ResNet-50, and ResNet- 101) across all seven datasets in Table 12, with corresponding visualizations provided in Figure TO We observe that larger models tend to have slightly higher utility (overall accuracy or AUC), however, this improvement is not consistent in the disadvantaged group in terms the worst accuracy/AUC. For fairness metrics, larger models exhibit varying trends across datasets without an obvious pattern. The results suggest that increasing model size does not offer a reliable path toward improved fairness for single-modality classic supervised learning.
C.1.6 SENSITITY ANALYSIS
While the previous sections analyzed training choices in isolation, Table 13 provides unified quantitative comparison of their relative impact. We aggregated the standard deviations from Table 9 10. and 12 measure the model's sensitivity to that specific choice in Table 13 Our results reveal that optimizer choice yiclds the highest standard deviations across the majority of datasets and metrics (23 in 35). This aggregate view reinforces our earlier qualitative findings. First, optimizer
34

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Published as a conference paper at ICLR 2026
100
100
ColesA FalrFace HAMI0000 Watertirds
Colebd -aarfacce
HAM10000
Waterbirds
Utily
Worst
75
70
70
03
65
Reshitt-1 18
Reshict34
ResNett 50
ResNet-101
ResNet-18
ResNet:34 34
RetNect 50
ResNet-10
Model size
Model size
FarFace
HAM10000 Waterbirds
-Fi217k
8-
EqDda
Cap
75
70
2-
ColobA Fairfaace HAM10000
Waterbirds
UTKFace -Facet - Ri2
ResNet-11 18
ResNet-35
ResNet-50
ResNet-101
ResNet-18
ResNett-34
ResNet--0
ResNet-10L
Model size
Midel
Celcod
-Farfacc
HAM11000
Waterbinds
UTKRace
Facett
ResNet-11
Reshet-34
et-00
ResNet-101
Model
Figure 10: Effect of model size on utility and fairness metrics across seven datasets, derived from Table [2 Each subplot corresponds to one evaluation metric, and curves represent different datasets. The x-axis uses ResNet depth (18/34/50/101) as evenly spaced categorical positions for visual clarity.
choice the most leverage-efficient knob for fairness-aware HPO: the search space is relatively small (a few commonly used optimizers), yet it leads most of the variance in both accuracy and disparity. In contrast, batch size does influence utility and fairness on specific datasets like Fitz 7k, but the potential search space much larger (a wide integer range), making exhaustive tuning significantly more expensive for lower expected payout in fairness gains. Finally, model size generally exhibits the lowest sensitivity, while increasing model depth significantly increases computational complexity. For fairness-sensitee applications under resource constraints, it is therefore more effective to fix moderate model size and allocate tuning budget optimization hyperparameters rather than model sizes.
35

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Published as a conference paper at ICLR 2026
Table 12: Performance comparison across different model sizes.
Dataset
Batch Size
Utiliy (Ace AUC)
Worst
Gap
EqOdd
DP
ResNet-18 18
86.57 0.18
83.76 0. 0.23
6.76 0.34
81.91 = 0.58
67.20 = 0.69
ResNet-34
86.56 = 0.03
83.55
0.11
7.23 + 0.18
81.04 = 0.09
06.81 = 1.19
CelebA
ResNet-50
86.47 + 0.18
83.50 + 0.10
7
80.01 + 0.40
06.44 + 1.25
ResNet-101
86.49 0.26
83.60 0.23
6.95 0.09
82.55 + 0.92
67. + 0.87
0.04
0.10
0.18
0.95
0.32
ResNet--1
92.75
91.78 0.61
0.66
97.62 1
94.55 1 1.20
ResNet-34
93.27 0.79
92.31
2.25
97.66 0.28
94.66
1.01
UTKFace
ResNet-50
93.39 0.73
92.50 0.71
2.05 + 0.82
97.85 +: 0.74
95.14
1.93
ResNet-101
93.70 1 0.44
92.92 1 0.56
2.03 0.700
97.58 1 0.30
94.66 1 1.10
Std
0.34
0.41
0.11
0.10
0.23
ResNet-18
66.76 0.21
66.34 =: 0.37
0.87 0.51
96.22 = 0.57
97.61 = 0.41
ResNet-34
68.22 0.17
67.80 0.18
0.90
96.33 = 0.55
97.46 = 0.25
FairFace
ResNet-50
67.33
67.06
0.58 0.344
96.34
97.57 0.32
ResNet-101
68.68 0.64
68.31 0.62
0.79 0.31
95.97 + 0.68
97.38 + 0.38
Std
0.75
0.75
0.12
0.15
0.09
ResNet-18
67.55 1. 0.37
64.25 1. 0.97
4.31 1.13
96.47 1: 1.23
96. 1 1.12
ResNet-34
67. 66 = 0.20
63.33 = 0.63
5.64 + 0.82
76 0.85
96 98 + 1. 25
Facet
ResNet-50
67.85 0.79
63.99
5.03 0.97
96.95 = 1.75
96.20 = 1.56
ResNet-101
67.
63.85 1.41
1.03 0,47
0.26
0.41
Std
0.11
0.34
0.56
Re
88.35 + 1.83
84.68 2.02
4.11 2.08
88.17 + 3.10
82.22 + 4.78
ResNett-3
87.44 1. 0.26
83.35 1.93
4.87
2.14
88.33 1 2.96
82.08 1 4.71
HAM10000
ResNet-50
88.35 + 0.89
83.94 2.61
5.49 2.82
84.33 + 6.83
79.63 + 6.81
ResNet-101
89.01 + 1.74
85.44 3.52
4.69 +
89.67 + 3.34
84.84 + 2.58
0.56
0.49
ResNeet-18
19.74
88.39
2.92 1.14
94.92 2.48
94.46 1.40
ResNet-34
88.78 0,26
87.96 0.16
2.60 +: 1.12
96.09 + 1.21
95 08 1.88
Fitz17k
ResNet-50
89.59 = 0.50
88.77 + 0,88
2.35 + 1.57
29 + 1.50
95. 45 + 1.70
ResNet-101
89.89 = 1.10
88.27 = 1.82
3.63 0.62
95.19 = 1.82
94.80 + 2.10
Std
0.43
0.29
0.48
0,58
0,36
ResNet- 18
85.63 1.36
84.20 0.94
2.87 0.85
66.53 + 3.31
77.67 + 3.52
ResNett-4
89.21
87.29
1.62
78.34
2.47
Waterbirds
ResNet-50
90.27 1.28
88.51 1.92
3.52 1.39
80.55 - 3.71
84.62 1 3.22
ResNet- 101
90.09 0.22
88.51 0.88
3.16 1.86
80.59 1.90
84.85 + 1.23
Std
1.87
1.76
0.37
5.83
2.89
Table 13: Standard deviations comparison across different training choices.
Dataset
Tuning
Utility
Worst
Gap
EqOdd
DP
Optimizer
0.21
0.35
0.43
1.00
0.67
CelebA
Batch Size
0.05
0.07
0.09
0.35
0.19
Model Size
0.04
0.10
0.18
0.95
0.32
Optimizer 0.38
0.50
0.51
0.46
0.45
UTKFace
Batch Size
0.21
0.36
0.41
0.41
0.51
Model Size
0.34
0.41
0.11
0.10
0.23
Optimizer 0.69
0.89
0.49
0.20
0.13
FairFace
Batch Size
0.64
0.68
0.40
0.12
0.17
Model Size
0.75
0.75
0.12
0.15
0.09
Optimizer
0.52
0.72
1.11
0.95
0.69
Facet
Batch Size
0.19
0.53
0.52
0.58
0.92
Model Size
0.11
0.34
0.47
0.48
0.56
Optimizer
0.79
1.34
0.68
1,75
2.88
HAM10000
Batch Size
0.53
0.96
0.73
0.80
1.23
Model Size
0.56
0.79
0.49
1.99
1.84
Optimizer 0.86
1.15
0.54
0.77
1.04
Fitz17k
Batch Size
1.22
1.28
0.17
0.50
0.71
Model Size
0.43
0.29
0.48
0.58
0.36
Optimizer
0.98
1.40
0.90
2.94
2.35
Waterbirds
Batch Size
1.52
1.89
0.83
4.38
3.60
Model Size
1.87
1.76
0.37
5.83
2.89
36

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Published as a conference paper at ICLR 2026
C.1.7
IMPACT OF MODEL SELECTIONS
Table 14: Comparison of model selections. M denotes selecting based on the maximal overall utility.
CelehA
UTIPPACE
PairFace
Farel
ACT:
Winer
Cup
ACE:
Wore!
FijEM
T1P
ACC
Were
Cap
Wend
BRM M
85.56
63,00
6:90
81.50
67.48
93.78
11. 85
07: 22
94.65
05.35
06.31
B.57
15:22
07.0
07.98
66.15
4.50
96.18
95.95
43.76
6.76
67:30
91.78
2,36
97.62
65.76
66.34
0.57
96- 47
Randhuy BWH
85.79
63.50
6.81
66.99
42.09
97.42
1.48
07.00
07.53
64.44
16
97.91
Gisepb60 34
86.14
83.51
0.22
76.76
66.22
02:11
91.43
2.18
97.46
34,40
65.11
1.64
96.53
97.86
07.32
64.19
4.0
00.07
98.16
GieapDeoo-EW FWH
85.12
63.50
6.31
75.75
66.41
92.45
91.41
2:44
90.05
94.77
05.31
45.22
0.60
09.31
07.47
07.20
04.07
4.08
93.76
95.36
Datest
ACC
Wors:
Cup
EQ0U
DP
ACC
Wess:
Gup
EqDAM
DP
.CCC
Werk.
Gup
E4CAM
DP
BRM-M
85.21
5-31
88.45
5.01
91:22
35.53
R3.65
2.55
19:.3
72-66
BantAag-MM
89.55
85.35
4.63
65:31
91:31
50:13
2.37
15:36
91:18
87.48
K5.60
5.76
72.12
$1.35
Rund/ug PWH
89.00
84.67
4.91
85.43
$4.73
91.29
90.15
2.51
95.61
94.53
85.09
84.02
3114
08.09
77:25
GieepD80-FWW FWH
87.50
83.35
4.08
98.94
$3.86
90.72
98.06
192
91.36
95.24
85.40
84.45
102
07.31
75.01
We use ERM, RandAug, and GroupDRO as examples to compare the model selection methods in Table 14 since their differences are more obvious to observe compared with others. The DTO- based selection prioritizes models that maximize utility for all groups, ensuring that no subgroup is disproportionately disadvantaged. On the other hand, the FWH selection emphasizes fairness without significantly harming utility based on the DTO-based method selected ERM results, focusing on reducing disparities without major accuracy degradation. The M the Table denotes using maximal overall utility. FWH selection achieves smaller fairness gap across multiple datasets. These results show that prioritizing fairness constraints does not necessarily mean sacrificing model performance, long careful selection strategies are employed. However, since the selection is performed on the validation set, it still faces the potential trade-offs when validation-test discrepancies exist.
C.2 COMPUTATIONAL OVERHEAD COMPARISON
In this section, we compare the computational overhead of various methods in terms of additional model parameters and overall computation times. Since methods like FIS perform additional computations during validation, we measure the total computational time, including the training, validation, and testing phases. Notably, because FIS requires computing the gradient for each sample, we use PyTorch's ' vmap to accelerate the computation, which aligns with the official FIS implementation in JAX.
In Table [5] we report the additional parameters required by specific methods and the average computational times (in seconds) for the evaluated methods. Most methods use the same number of parameters as the standard ERM baseline, so we focus only on those that introduce extra model parameters. The reported times averaged over 5 runs, including the training, validation, and testing phases. For datasets with a limited number of samples, time differences are negligible and thus omitted from this comparison. Although some methods, like LAFTR, exhibit significantly increased computational demands, they do not outperform simpler methods. This highlights the need for easy yet effective approaches that balance performance with computational efficiency.
Table 15: Average computation time in seconds for round and the extra number of parameters introduced by specific methods. DFR not included here since it is a post-processing method.
ERM
RandAug
Mixup
Resampling
BM
FIS
Parameters
1024
CelebA
113.06 0.25
117.06
1.95
112.48 + 1.19
112 80 0.38
114.08
1.30
145.29 + 0.15
UTKFaaee
30.33
1.80
30.55 +
0.53
38.83
1.12
39.70
1.25
41.65
0.57
Fairfaace
54.54 1:541100 0.31
54.53
0.48
54.90
0.34
54.68
1 0.30
55.40
0.38
72.07
0.14
Facet
0.34
0.26
26.53
0.200
35.14
Avenge
58.34
59.42
58.13
58.21
59.12
73.54
Decompled
LAFTR
ESCL.
GapReg
MCDP
GroupDRO
Parameters
1024
65921
CelebA
114.24 : 0.62
175.77
0.86
119.75 :: 2.06
112.92
0.71
112.94
0.95
113.75 :: 1.39
UTKFace 38.09 0.95
40.56
1.10
17.62
38 38.94-1 0.13
97
0.75
39.00 1000011 1.00
PairFace
65.35 4 0.61
83.76
+ 0.41
0.94 54.81
55.63
+ 0.54
03.03-1
0.35
55.31 + 0.66
Faest
26.74 0.18
39.61 0.18
26.19 1119::00
26.85 + 0.25
82
0.28
26.71 = 0.16
Average
58.76
84.93
02.09
58.50
58.59
68.69
37

---

Published as a conference paper at ICLR 2026
C.3 LARGE VISION-LANGUAGE MODEL EXPERIMENTS
C.3.1 MODEL SCALE IMPACT ON FAIRNESS
Table 16: Effect of LVLM model scale on accuracy and fairness metrics
Markc
150
32B
120
22-11B
2-2
4-Gca 1083
10.221133 2.34
P2.04 (U21
8.73
235
1.59
5.00
17.09 1130
THl
0000 14.77
53.00
50.27 4.000
20 2.334
$3.994 8.30
10.29 122
8872.400 9.43
10.9 147
66.00 6.60
94961 835
54.51
50.00 1000
14.00-0000
78.30
54.74140
MLC
01.00
10 0.04
10.12 0000
07 11022
100
50 00:36-33 9:33
16.00 8:00
17 17:32:01377
Wean
00.73
0.20
06 2:12
00.0 0.20
00.03 0.25
0.00
06.04 0.22
9631 0:34
2 8:26
1.21
130
251
1,22
1.1
30
1-2-1-1
00 1.00
1330
00.28
50.23
14.004 1.31
1:00
90.1 1.1M
1100
53.10 1:01
hattec
240
125
0:13-011
1000 0000
00.000001
6.00
00.08 0.13
09.00 450
472)4000
06.76 000
15.5 2074
16.74.0022
00.1
63
0406 1:3
03.53 0:10
4101
5/1-1:31 1.31
172 +6.6
3P
1:54
9630
00000
1.00
1.5
15.41 0.00
72/24/19
11:31-0:0
36:5-+1337
626-016
00:00-0000 0100
8120-030
06:51 000
9102-000
000
18.19 0m
71:5-+331
0100
A1:0-1:00
16:12-000)
14.0 11.0
1117-914
(610 0:0
06.15
09.35 10.30
76.78
17.36
2531
30.00-1000 14.0
67.34 3131
75.88
1996
7451-1640 12:14-15:9 17.90
200 300
16.4
West
04:33
53
21.3021130
04.56 24
1245 25.0
2518-2244
00.11 15:00
c.5 21.39
75:0 2038
15.31
03.22 1601
2611-170
Auraaa
City
2:30
4.0
138 9:07
31
18:11:1
1008
2:0:1:.0
330 5:10
3186 00.5
110 1137
348-200
14.17
12.79
1471-1.0
33:01:349
15
436
01.8
In this paper, we include four families of vision-language foundation models: LLaVA, Qwen-VL, Gemma, and Llama at multiple parameter scales (from roughly billion to 90 billion weights) to probe whether the standard "bigger-is-better rule extends to fairness (Table [16). The averaged results confirm that scale almost always boosts utility: mean accuracy climbs steadily within each family (e.g., LL 68- - 71 %, Gemma 67 - 76 %), and worst-group accuracy rises in the same trend. Larger models also score better on our parity metrics like demographic parity (DP) and equalized odds (EqOdd), which suggests that they become more confident and consistent across groups. However, larger models do not guarantee a better accuracy gap. For example, the gap more than doubles (3.9 7.3 9) for LLaVA. In short, increasing size a reliable path to higher accuracy and better parity rates, but it does not guarantee smaller disparity between groups; in some cases, it even amplifies it. These mixed trends highlight that parameter count alone cannot close fairness gaps where architectural choices, alignment strategies, and dataset biases remain decisive factors.
We further select the largest variant from each model family and present the results in Figure Among them, Qwen2.5-VL. 72B demonstrates the best overall balance between fairness and utility. Notably, Gemma 27B achieves comparable performance to Qwen despite its smaller model size, while LLaMA 4-Scout, although achieving strong headline accuracy, remains susceptible to dataset- specific biases.
C.3.2 PROMPT SENSITIVITY OF LVLMS
Fairness evaluations of LVL.Ms often rely on handcrafted prompts, raising concerns about robustness to prompt variations. To examine the sensitivity of I.VIMs to prompt wording, we design two paraphrased variants for three dataset tasks (we consider those with relatively larger disparities) in addition to the original instruction (Table 17). and evaluate whether performance holds consistent across different formulations. Results in Table 18 show that, across most cases, models exhibit broadly consistent fairness performance under different prompts, with only a few extreme cases (e.g.. Qwen2.5-Vi VL on CelebA) showing large deviations. This indicates that prompt rewording alone does not fundamentally resolve fairness issues, LVLMs remain subject subgroup disparities regardless of the prompt design.
38

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Published as a conference paper at ICLR 2026
ACC CelebA
Worst
LLaVA 1.6 348
CelebA
LLaVA 1.6 348
Qwen2.5-V6 VL-22-5-VLL-1 728
Qwen2.5-V4 -- 728
20
80
Gemma 27B
Gemma 3 278
09
Liama Scout 1098
Liama 4 Scout - 1098
40
40
Waterbirds
TFKace
Waterbi 85
UFKFcce
20
20
facet
Tarffce
facet
Taiiface
Gap CelebA
EqOdd
CelebA
LLaVA 1.6 348
LLavA 1.6 34B
Qwen2.5-VL 728
Qwen2.5-VV 728
Gemma 2-27
Gemma 3 278
60
Liama Scout 1098
60
Liama 4 Scout 1098
40
40
Waterbie
TKFace
Waterbi es
TKface
20
20
Facet
Tarrface
Facet
faiiface
DP
CelebA
Qwen2.5-VL-
40
Waterbies
KKacc
20
Facet
Fairface
Figure 11: Performance comparison of LVLMMs across five datasets and five evaluation metrics. For clarity, the Gap metric is shown as 100 Gap, where higher values indicate better performance.
39

---

Published as a conference paper at ICLR 2026
Table 17: Prompts used for evaluating L.LM sensitivity. Each dataset has the original instruction (Origin) and two paraphrased variants.
Dataset
Prompts
ColebA
Origin: Is the person in the photo wavy-haired? Answer 'Yes' for wavy hair, 'No' for
non-wavy hair.
Variant 1: Does this person appear to have wavy hair? Reply with Yes/No only.
Variant 2: Classify hair texture as wavy or non-wavy. Output: Yes or No.
FairFace
Origin: Based on the photo, classify the race of the person into: O: White, 1: Black, 2: Latino or Hispanic, 3: East Asian, 4: Southeast Asian, 5: Indian, 6: Middle Eastern. Answer with the category number only.
Variant 1: Pick single integer label (0-6) for race: O-White, 1-Black, Latino/Hispanic, 3-East Asian, 4-Southeast Asian, 5-Indian, 6-Middle Eastern. Output digits only.
Variant 2: Classify race using numbers: O=White, 1=Black, 2=Latino/Hispann
3=East Asian, 4=Southeast Asian, 5=Indian, 6=Middle Eastern. Answer with number only.
Facet
Does the photo show visible face? Answer 'Yes' if the face is visible, otherwise 'No'.
Origin:
Variant 1: a face clearly visible? Reply Yes/No only.
Variant 2: Face visibility check: Yes or No? Output one word.
Table 18: LVLM performance under different prompts. Each model is evaluated with the original instruction and two paraphrased variants. For CelebA, Llama3.2 often produces non-conforming answers under Variant 2 instead of the required categorical outputs; is therefore omitted from the reported results.
400
04.00 0.15
23 54 EL17
20.50 0.54
42.88: 0.11
$3.47 0.11
53.09 2.00
(4.33 0..26
05.75
14.72 B.15
22.04 0.2.
$1 SLSS--5 0.22
Wasi
28.60
0.50
0:35
51.10
10:10:0000 0.31
27.79.800 0.17
05.10:00..55
15.22 0.00
66.17
00000
01.5 6:57
08.66 8:3
75.00 0.45
10:00:0:3
CzabA
Gap
12.55
0445
12.00
1-41
5.81
1:00
21.03 1152
11.0
23.312-110
1.33
3 1.135
23.54 0031
00 10:01
E40Mi
12:44
0.17
14.23 1121
91.04
0131
93.20.21088 1154
50.01 0.12
13:75 2-10
8..2
00.24
0:20
05.33
5/23
DF
23.15
0134
07.08 0144
90.33 1122
91.20 1.13
99.0020012
54.1 2400
96.1
92.58
8099
70.38-10331
1155
(7:44.00) (1)
12.01 1.96
17.56..00
05.37
11.00
11.1
40C
00:0 0.25
0115
51.20:10445
6230121113
17.30
2011
06.3: 6:27
00.55 Bv47
01.7111441
51.0121100
15:0:1.1000
Waxii
02:50:1111 1.11
01:28 2035
1455-21007
11:00:1333
12.002115
0433
E/1
00.08 0:25
01:2-1044
9116-11117
35:00:20.0
Roo
Ogr
1:0
4.24 2018
0.6 1.30
116-2111)
1.5
2
141 10100
475
0.22 238
Ea000
18:15
0.40
17
1173
6.40
18:37
10.77
1.50
1118 1185
11.5
96:3 3.14
94.77 3.42
9458
17.22
1157
96.7 0032
15:07 11117
6.21
05.82
95.70.1..75
11
40

---

Published as a conference paper at ICLR 2026
C.3.3 OPEN-ENDED GENERATION WITH LVLMS
To complement the closed-set classification prompts used above, we also evaluate LVLMs on open- ended generation tasks. We use UTKFace, FairFace, and Waterbirds, replacing the classification prompts in Table 18 with open-ended instructions encouraging unconstrained image description:
UTKFaae: "Describe the person in this photo, including their apparent gender presentation and any other observable characteristics."
FairFace: "Describe this person's ethnic and cultural background, including physical features, cultural markers, and any other relevant characteristies you can observe."
Waterbirds: "Describe this bird in detail, including its species characteristics, habitat preferences, and behavioral traits you can infer from the image."
We use the same models and evaluation splits in previous experiments. To make the results comparable to our glassification-bassee evaluation, we post-process the generated text into a prediction in the original label space with small lexicon label-specific keywords and common synonyms.:
UTKFace: words such as "man", "male", "boy", "gentleman" vs. "woman", "female", "girl", "lady".
FairFace: terms such as Whhite/Caucasian/Euuppeee "Black/ African American", "Lati- no/Lattn//Hisannie,, "East
"Southeast Asian/Thai/Vietnanees Filipino))", "Indian/South Asian/Desi", and "Middle Castemm/Arab//eeriian...
Waterbirds: class names and taxonomic descriptors used in Sagawa" et al. (2020); Wah et al. (201I), including albatross, auklet, cormorant, frigatebird, fulmar, gull, jaeger, kittiwake, pelican, puffin, tern, gadwall, grebe, mallard, merganser, guillemot, and Pacific loon. We additionally use coarse- coarse-grained keywords such as "aquatic bird"' "waterfowl"" versus "landbird") "terrestrial bird" to supplement the class matching.
We use the same models and evaluation splits as in prior experiments. To map generated text back into the original label space, we apply ineeeissneeeivee keyword matching using small lexicons of class-specific terms and common synonyms; these cases constitute small fraction of the data.
Table 19: LVLMs comparison under open- open-ended generation.
Dataset
Metric
LLaVA-1.6
Qwen2.5 5-VL
Gemma
Liau
78
138
34B
78
32B
73B
4B
27B
3.2.118
3.2.908
4-Scomt-10998
ACC
92.24
92.93
95.38
97.26
97.31
96.14
96.37
96.83
96.71
97.38
98.02
96.09
Wons
91.99
91.98
94.80
96.94
96.87
95.07
95.86
96.
96.09
96.62
97.17
94,97
UTKFace
0.59
2.15
1.32
0.72
1.01
2.44
1.95
1.24
1.42
1.73
1.82
EgOdd
99.09
97.41
98.57
98.84
98.90
97.32
97.96
98.65
98.53
98.23
98.07
97.15
DP
95.58
94.44
95.40
94.32
94.87
93.69
95.33
95.3
96. 13
97.27
92.69
92.69
ACC
$1.93
60.88
$1.29
$1.58
52.91
Worst
51.47
40.43
60.30
50.86
$1.32
51.26
50.08
FairFace
Gup
0.88
2.94
1.24
0.92
0.55
3.18
6.07
EgOdd
97.08
96.
V5.
DP
96.16
97.85
97.76
97.85
97.78
97.51
98.12
ACC
82.10
85.63
53.84
95.63
95.16
95.89
92.59
93.65
94.56
92.90
95.23
93.35
Worst
76.48
82:30
78.50
94.34
95.01
91.07
92.21
93.24
90:70
93.86
91.76
Waterbirds
Gap
11.59
7.02
10.82
1.25
1.65
1.76
3.08
2.99
2.70
4.44
2.75
3.20
EgOdd
80.79
81.93
79:12
95.11
91.38
96.73
93.27
92.22
93.91
92.70
96.38
92.94
D>P
77.61
95.21
96.54
92.40
90.30
95.
As shown in Table 19 the open-ended generation exhibits utility and fairness trends that closely mirror those observed in our LVLM classifications. Although accuracy generally increases or remains stable with model scale, fairness metries such as Gap, EqOdd, and DP do not improve monotonically. For instance, on UTKFaae, the Qwen2.5 sees an increase in the gap, despite high overall accuracy.
Furthermore, we observe that certain model families frequently refuse to generate descriptions for FairFace: LL. exhibits refusal rates of 31% (7B), 26% (13B), and 47% (34B). The trend is even more pronounced in the Llama 3.2 series, where aggressive safety filters result in refusal rates of 81.1 % for the 1IB model and 95.0% for the 90B model. Consequently. the remaining valid outputs insufficient for robust faimess assessment, highlighting critical trade-off: while safety alignment is essential, its over-application can render models untestable for demographic disparities in open-ended generations.
41

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Published as a conference paper at ICLR 2026
D ADDING NEW DATASETS AND ALGORITHMS TO THE FRAMEWORK
Our benchmarking framework provides a flexible and extensible design for integrating new image classification datasets, new dataset can be inherited from Fa rDDtaaset and specify image folder location, target labels, sensitive attributes, and an indices list to split the dataset. The core dataset class, PairDataset, presented below.
class FairDataset (Dataset)
def
feit_(self, root, split='train', transform:None, seed=42)
Base folder and images directory
relf root
self.ing_dir None
self.split split
self.indices [] Indices for the split dataset
self.sensitive_attrs np. ap.array((])
Sensitive attributes
self.targets = np. (r)a # Target labels
self.transform transform $ Image transformation pipeline
self.images self.mmage_i1e_1151 list []
Option to store images in memory for faster access self images
By inheriting from ERM, this new class has access the default model, optimizer, and loss function. A new method needs to be implemented to rewrite the training process, ensuring that aligns with the new algorithm's objectives. For example, the MCDP class extends ERM and introduces fairness-aware loss function.
class MCDP (erm)
def init iniiiintselll (self, args):
super () init_mni___ (args)
falr
MaxCDFdp (args. wwwgg...d_..eeneooe..o. tempettempr)t)rrr))
def train train_loader, epoch, args):
self for batch_index, (data, target, sensitive_attr)
(train (tadei__ output = self. (data)
self. zer grad() fairloss lodd__aad self. fair_lesa loss (output,
scnsitive_attz)
loss = self.criterion (cutttur target) fairloss
loss. backkard
self. self.optimizerr.teer
42

---

Published as a conference paper at ICLR 2026
E SUPPLEMENTARY RESULTS
Here we present the standard deviation for Table2ann and Table 21 below:
Table 20: Full results on seven datasets with standard deviations for vision models.
Receepling
Decoepdel
La
PSCT
Greaption
Ouadae
Sum
04844
5-33m
10.00 6:77)
157
17
0730
03:51
9051
9000
750 S-
00
9:30
07208
A00
03:75
9-70-0.3
16:72-100
Weil
61.
3:61
00.00
0100-000
9000 -025
01.0-003
01.02
01:0--000
11:1--1:0
UTKK
<kyg
17.52.:035
27.022100
10:44:1333
3.00-3372
180
18:01.00 1.0
3:31+399
4000
16:70 33:
06:0
-100
06.00-010
178-055
05.07
0816
66:72
0533-931
651-0035
1-15-101
5:45
0:15-001
00
30
57.31 0:44
07.08
17.82.034
90.311111
300 11.56
56.31.833
8:3
17.51
2-22
18.58
1
1123
Weei
14.25
01.4
108
1431:111
200.000
02
2354
01.30
10.10::110
Pact
<kg
0
1.15
375
110
1.4
415-21155
133
560:2141
637
322 138
311 2:121
1000-100
638
110
64.35
2010
1.00
K-9
Weei
14.5
2.35
13.41.10
13
08 1:11
51862
10.0
HAM00000
16.77
3:31
L1
11:44:141
13:3
415
14:1
5LB 5.5
01.08 1.5
11.27-1999
107
66.73
230
15332:55
155
00.0
2:22
15.41.
433
277
11.11
11:05
17.54 111
18:36 110
51992371
1:00
Weel -1
85
001
1:10-116
1000-10
6---111
85
-005-115
1:13
50:0-781
80
26.07 1-3
FRaTTE
2400
<ky
14.2
2:65
351
2:5
11.
0:2-1:0
1:3--131
241
555-516
122-3331
161-50l
10
100-100
3:5-1444
ACD
55.551130
13
-1:0
17.77:100
0738
2:177
S.B.2:59)
0058 112
85
0900
::44
1127--0177
13.91:13
51.0
6.8-2371
333
5.77--0
51.01171
Mutterinn
3:1
0016-200
134
33331.2335
(2.11 415
47.8:2440)
17:31:2211
(199-199
30
152
1725
-3/
11011419
960
110
233
76.0
1.
41.
935
1677
16114112
0609
07.3524932
Table 21: Full results on seven datasets with standard deviations for multimodal models..
Dataset
Metric
ERM
RandAug
BLIF2
CLIP
Fairer- CLIP
CLIP- SETD
LLaVA-LL 34B
Qwen2.-VVL 7381
Gemmad 27B
Llanu4
Scout
ADC
5637
0.18
86.72 s 0.19
47.38 + 0.23
T4.07 :: 0:41
71.75
0.34
7245 6: 0.27
44.83 e 0.00
03.76 a: 0.00
74 : 0.00
83.71 a U.15
Wint
53 36
85
36,97
07.43 0.51
6732
0.55
06:36-
1.01
32.09
05.00 4
72:18
8655 4 0:21
Celeb
Cop
6.70
15.56 0.30
1.20
5.47
ligUid
51.01
= 0.58
81
1.32
07.24 0.5
81.72
0: 0.58
0.779:00778
10. 00.70-1 1.77
97.43 0:30
02.00 0:33
92 0.775
81.8 4:
DP
67
- 0:08
67:37
6 0.42
05.00
4 0.05
61.
+ 0.00
51.06 0.58
93 6 9.28
0:15 91.99
01.71 9 0.30
74.53
6 0.50
71:50
0.10
ACC
92.75 0.54
99.19
5: 0.31
94.23
2 0.40
06.72 s: 0.41
90.79
0.97
96.70 0.33
97.12 0: 0.19
96.78
4: 0.35
97.25
4: 0.46
97.02 à: 0.17
Wosst
0.61
92.19
96.05
0.08
96.03
0.12
05.76
0.442
96,89
UTKFace
Cap
2.30
0.05
2.34
0.50
0:45
0.38
1.00
0.80
1.72 0:22
1.55
1.51
0:00
3.98
0.31
0.85 0.221
1:00 0.37
EdDal
07.02 0.66
97.02
:: 0.46
09:24
1: 0.36
07.06
1: 0.62
96.17 0.24
98.96
:: 0.16
98.16
: 0.24
97.50
1: 0.82
59.20 1: 0.21
95.27
0:20
DP
94.0 1.20
94.83
:: 1.12
95.61
4: 0.33
00.91 =: 1.11
94.27 =: 1.17
94.96 2: 1.12
96.22 = 1.50
04.33 2: 1.63
96.25 :: 1.11
94.00 1: 1.11
ADC
0676-6.21
0.21
65:37
59.57
0.00
57.36 0.00
56.1 0.000
59.73
0.10
00:1 0.00
03.57 0.01
59 10-30-1100 + 0/75
56.57 0.17
67.00
56.74
0.00
51.50
61.75
56.71
53.53
FaieFaoc
Cop
0:5
0.8
3:00
0.00
0.31 i: 0:00
0.86 000
0.54
1.86
000
1.79 0.03
5.71 0:100
633
0.38
Righal
9632- 6.57
96.14
i 0.16
93.90
+ 0.00
07.16
+ 0.00
96.75
97.11
+ 0.12
96.22
0.00
06.00 + 00000
95.13 + 0.96
96.51 0.11
DP
0.41
97.55
:: 0.24
96.89 +: 0.00
08.50 :: 0.00
96.32 :
96.46
0.00
96.07
: 0.00
97.57 5: 0.00
97.16 :: 0.00
95.00 1: 0.05
ACC
0.37
67.83
41.16
23.10
30.17 0.06
33.25 0.20
0.20
05.41
07.73
07.04
Wond
64
26:25=:
61.01 ::
40.40
SL.43
$1.60 0.10
31.64 0.10
57.97 0.55
03.70
67.25
61.1
Pacet
Cup
4.31
1.13
8.78 0.97
8:23 0.91
6.52 4: 0.54
7.37 & 1.22
0.72
2: 0.56
6,04 4: 1.18
1.42 1.21
3.83 0.50
96.47
1,20
96.05 + 1.02
03,02 d: 1.44
06.72 di 0.22
98.82 =
0.07 90.72
95.00 =: 1.41
07.04 4: 1.15
96.22 d: 1.00
07.00 d: 1.09
ACC
86.09
62.50
78.06
0.00
75:20
90.77
85.62
Wond
$0.00
0.94
84.57 + 9.53
39-16
0.00
74.53 000
731-19 0.00
79.81
0.56
26 0:00
01.27 0.09
88007 + 0.11
85, 14.7441 0.17
Witerbirds
Cop
2,57
0.855
5.1.
+ 2.50
26.23
+ 0.00
7.01 = 0.00
8.50 6 0.00
5:76 6 0.07
11:39
= 0.00
2:58 0.00
5.59 0.55
5.76 0.16
EgDiil
50.53:3.51 3.51
68:09 :: 2.52
66.24 0.00
73.96 :: 0.00
78.19
05.67
à: 0.10
50.8 = 0.00
91.03 3: 3:000
92.95
1: 0.48
86.38
à: 0.15
76.12
70.75
98.09
0.009
95.17
0:116
89.64
AUC
38.05
80.09 à:
35.67
10.15
51.88 2.00
02:03 0.94
Woest
54.55
2.00
84.7
:: 3.29
25.00 +: 4.95
50.56 =: Ln12
12/11
=: 3.98
33.41
:: 4.66
HAM10000
Gop
4.31
2:0
4.39 a: 3.70
0.99 4.11
3.12
3,32
d: 2.55
2.24
2.13
EdDai
3.31
88:43
96.75
96.19
52:22
84.33 à:
07.90
00.00
98.94
08.02 0.52
AUC:
50.74
91.20 10. i 0.32
07.08 + 1.79
0.83
09.81 (..00
6937 0.50
Wind
56:30
1.06
$0.15 :: 0.36
06.06
4: 1.95
09.76
: 0.59
60.55 0.11
69:35 :: 1.71
Fitz1%
Gup
2:02
1:14
0.00
8.74
1.68
2.51
2: 1.54
2.52 2: 1.00
4.75
1.3.2:10 4.08
Rynai
07.00
89.57
P6
1.40
91.53 ::
95.00 h: 1.66
05.08
1.96
08.02
02.19:::..4 0.61
43

---

Published as a conference paper at ICLR 2026
90
Overall Acc
Overall Acc
80
75
80
Gap Acc
Worst Acc
Figure 12: Intersectional analysis on UTKFace using race age groups under different hyperparame- ter settings. Each point corresponds to different configuration. Appropriate hyperparameter choices can reduce the gap while maintaining comparable overall accuracy.
F LIMITATIONS AND FUTURE DIRECTIONS
Despite its breadth, NH-Fair is only intermediate step toward truly comprehensive fairness benchmark, yet several gaps remain:
Dataset: Although span three domains, most datasets are still for human faces, due to the scarcity of open, well-annotated fairness datasets. Some datasets include multiple protected attributes. Due to computational resource limitations, we did not fully use them but selected the attribute with a clear performance gap. Future work may consider intersectional groups (c.g.. gender race) and more visual domains. We conducted small-scale intersectional analysis UTKFaaee where we formed groups by combining race and age (below 60 and above 60). The scatter plots in Figure 12 show that intersectional biases might also be severe, but rigorous hyperparameter tuning can significantly mitigate intersectional disparities at comparable overall accuracy. Extending NH-Fair systematically cover intersectional groups (e.g., gender race) across more datasets and to include additional non-face visual domains is direction for future work. In addition. our zero-shot evaluations cannot guarantee that images were not used during the pretraining phase of LVL.Ms, which may lead to potential data leakage.
To bridge single-modality bias mitigation algorithms with multi-modal models, we limited experiments to single-image classification. Segmentation, detection, and captioning could be further explored with task-specific fairness notions.
Task scope:
Fairness Notions: As stated in Section 2 there are also other fairness notions to quantify the model fairness, like individual fairness and counterfactual fairness, which could be used future experiments.
Experiments: We evaluated LVLMs in zero-shot only. Fine-tuning them could further reveal their fairness performance and bring more valuable insights. In the comparison, we divide the method into data-centric and algorithmic. However, combining all algorithmic approaches with data-centric approaches evidence, we evaluate the combination RandAug (data-centric) with three algorithmic debiasing methods, and we report results in Table 22 On UTKFace. integrating RandAug with MCDP and GroupDRO improves both utility and fairness relative to ERM, achieving tairness-without. harm. However, this improvement does not consistently generalize; for example, on HAM1 0000 the same combinations not yield similar benefits. These mixed outcomes suggest that pairing data-centric and algorithmic methods is promising direction, but its effectiveness is dataset-dependent and requires broader validation. We consider a more systematic investigation of such combinations to be important avenue for future work.
promising solution to achieve better fairness. To provide preliminary
Analysis of LVLM Fairness: The paper considers faimees differences across LVLMs with different architectural or alignment choices but does not account for confounding factors like differences in training data or finetuning protocols due to the lack of transparency and standardization in
44

---

Published as a conference paper at ICLR 2026
recently released LVLMs. For example, the Gemma 3 Technical Report discloses only high-level information, such as the number of training tokens and the fact that both SFT and RLHF were used for alignment. Further details of implementation comparisons. This lack of visibility common current L.M and LVLM releases, making some in-depth analysis difficult. As result, our study intentionally focuses aspects that quantifiable and reproducible.
unavailable, which prevents controlled
Table 22: Preliminary results of combining RandAug and algorithmic methods. (RA RandAug)
GapReg
MCDP
GroupDROO
Dataset
Metric
ERM
Base
+RA
Base
+RA
Base
+RA
ACC
92.75
0.54
12.33 1: 0.88
91.43 1: 1.07
92.49 1: 0.72
92.92
0.83
92.45
0.39
92.86
0.40
Worst
91.78
0.61
91.70 1 0.95
90.73
1.14
91.63
1.14
92.20
0.91
91.41
0.62
92.17
0.41
2.26
0.65
1,91 0.59
1.63
0.49
2.00
1 0.99
1.66
1 0.69
2.44
0.98
1.60
0.56
UTKFaace
EqOdd
97.62
0.33
98.10 =: 0.61
97.88
0.52
98.04
0.97
98.05 = 0.69
97.06
0.63
98.31
0.55
DP
94.53 +: 1,20
95.30 +: 1.62
95.82
1.55
95.80
0.64
96.42
1.68
94.78 1.85
95.15
0.88
AUC
88.35 1.83
S4.97 +: 3.18
85.55 + 2.27
82.96 1.84
87.33
2.12
87.06 = 2.72
87.08 .885
82.37
81.60 2.12
1.70
84.35
83.98 3.00
80.96
4.11 2.08
3.07 2.34
4.85 + 2.48
3.10 2.25
3.35 + 2.30
4.98 2.51
3.37 2.55
HAM10000
EqOad
88.17
98.15 3.71
96.98
99.52 0.95
5.88
90.94 5.33
87.70 5.06
82.22 4.78
96.74 + 6.32
97.40 2.72
99.58 0.85
86.56 6.11
83.86 5.70
80.31
G BROADER IMPACTS
NH-Fair provides a transparent and public benchmark that lets researchers and developers evaluate both single-modal and vision-language models under the same fairness metrics, motivating the community to build more fair machine learning models. However, the visibility also may lead to "benchmark gaming": model can be fine-tuned excel on the reported sub-groups while quietly marginalizing untested or intersectional minorities, posing potential downstream Al-safety concerns.
H LLM USAGE
In accordance with the ICLR policy, we disclose the use of large language models (LLMs) in preparing this paper. LLMMs are also a part of the subjects of our research, and experimental results are based on our own implementations and evaluations of these models. Separately, we used LLMs as general-purpose assistive tools for language editing, improving the elarity of writing. They were not used for research ideation, system design, or the generation or analysis of experimental results.
45


Reviews:
[{"review_id": "9DyWs0swd2", "reviewer": "Reviewer_RqaZ", "summary": "The paper presents a comprehensive study of fairness-utility trade-off in modern vision and multimodal foundation models. The authors create a new benchmark consisting of various domains and tasks, a systematic model selection protocol, and a set of group fairness and utility metrics. This benchmark is used to evaluate and compare SoTA models, training strategies and debiasing methods to explore the optimal recipe for accurate and fair models. Experiments show that a well-optimized baseline with large-scale pretraining and data augmentation matches or beats sophisticated mitigation methods in fairness while maintaining performance on utility tasks.", "questions": "See weakness section. In addition, I wonder if combining data curation and algorithmic mitigation can produce the best trade-off between utility and accuracy? Should future research explore the intersection between both, or prioritize data-centric methods as suggested in the paper?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- This work addresses several shortcomings in fairness evaluation in prior work, such as inconsistent hyperparameter selection, overlooked utility performance and not including pre-trained foundation models.\n- The evaluation framework is systematic, consisting of multiple datasets and domains, principled training and model selection, and numerous utility and bias metrics. This is crucial to ensure fair comparison between methods as well as reproducibility.\n- Extensive ablation experiments on training practices such as model size, batch size and weight decay, as well as evaluation of VLMs putting them in the same playing field with dedicated classification models.", "weaknesses": "- The proposed benchmark is limited to classification task. Despite being reformulated for image-text matching (CLIP) and generative (LVLM) models, it does not truly address utility and fairness beyond closed-set predictions. I would have liked to see open-set tasks like free-form image-text retrieval and open-ended VQA, captioning or reasoning, as these are the tasks where vision(-language) foundation models truly overtake task-specific vision models. This would also enable the holistic evaluation of VLM debiasing methods, which also tend to overlook utility performance while optimizing for fairness.\n- Even in classification, I am slightly concerned that the performance is already saturating for most models and datasets, with group accuracy gap under 5% and DP, EqOdd over 95%. I am not sure how perceivable these disparities are in practice. It might also be helpful to construct a \"fairness-hard\" subset with more challenging datasets/subtasks (e.g., more attributes in CelebA correlated with either gender; only \"wavy hair\" is used in the current version).\n- In general, I feel that the conclusions from the experiments confirm anecdotal observations bias mitigation (e.g., that hyperparameter tuning and model selection make a big difference), but not groundbreaking in context of existing fairness benchmarks (e.g., FFB https://arxiv.org/abs/2306.09468).\n- Certain conclusions in hyperparameter choice are qualitative (e.g., optimizer choice and learning rate are more important than other hyperparameters) and could have benefitted from a quantified sensitivity metric.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Bb0hWBkuaE", "reviewer": "Reviewer_ByuY", "summary": "This paper presents a new benchmark for bias mitigation methods across 7 image classification tasks. It benchmarks traditional mitigation methods with a ResNet18 as well as vision-language models like CLIP and LLMs. It also includes analyses on the impact of hyper-parameters on model fairness.", "questions": "Q1: Why did the authors select the datasets they did? Why did they include two medical datasets of the same modality (skin images) instead of considering other medical modalities?\n\nQ2: Are you comparing two model selection methods, or just applying DTO to ERM and then doing fairness without harm for the rest? Later you say “best model selected under DTO or FWH criteria”.\n\nQ3: Why did the authors not include post-processing methods? these are often suggested to be the most useful in practice, as suggested here [1].\n\nQ4: Why do you use resnet18 as a backbone?\n\nQ5: Why do you only look at group x class grouping for resampling?\n\nQ6: Is there any literature on optimisers and fairness? Can you explain your results? Similarly do you have a hypothesis for the effect of batch size?\n\nQ7: Why do you think there is so much prompt sensitivity - this seems like a significant weakness? Also for that table 17, it would be helpful to summarise the variation in results as SD or mean difference in outputs. \n\nQ8: How do you obtain SDs? Average over random seeds?\n\n[1] Oxonfair, NeurIPS 2024.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "S1: The authors conduct a systematic hyper-parameter optimisation and model selection protocol, a step which is often overlooked in bias mitigation benchmarks.\n\nS2: The authors present some unique analyses which are not usually discussed in bias mitigation papers, for instance on the impact of hyper-parameter choice on fairness metrics (e.g., choice of optimiser appears more impactful than choice of weight decay), whether pre-training is done (I liked the plots in the Appendix Fig 5), and the size of LLMs. \n\nS3: They conduct extensive experiments and include a range of model types. \n\nS4: I appreciated this insightful point: \"Over-reliance on datasets like Waterbirds may therefore underestimate the difficulty of fairness challenges and overstate algorithmic effectiveness.\"", "weaknesses": "**Major weaknesses**\n\nW1: My primary criticism to the paper is that I am not sure what the field needs is another benchmark suggesting that overall ERM performs better/more reliably than existing mitigation methods (I would say there is already a broad consensus on this). I would argue that actually, instead of having an aggregate benchmark where many methods/models are compared across different datasets and metrics, it would make more sense to do a tailored analysis looking at which mitigation methods work when. This is I believe in line with current research in the field [1,2,3,4] which suggest that it is important to understand what the cause of bias is, what type of bias it is, what fairness metric you want to optimise for, in order to determine if you should conduct mitigation and with what method. Similarly to this point, I think looking at so many fairness metrics at once is also counter-productive, as you will only really care about optimising one metric for a given setting (for instance there are very few settings where it would make sense to optimise for demographic parity) [5]. \n\nW2: I think the statement that \"the scaling law\" does not apply to AI fairness is over-stated given that the authors have only conducted a couple experiments on classification. \n\nW3: The authors say they \"suggest not confusing fairness datasets with domain generalization dtatasets (e.g., Waterbirds, Colored MNIST). While DG datasets probe robustness to distribution shifts, they do not contain socially meaningful sensitive attributes\". While I agree (as mentioned in S4) that it is important to not just evaluate mitigation methods on over-simplistic spurious correlation benchmarks like Waterbirds, I disagree that there is an inherent difference between domain generalisation and fairness datasets. I think what matters for bias mitigation methods is just to have some coherent grouping of data (and that can be based on demographic attributes or on other groupings, like where the image was taken, what device was used etc.). You can have super \"simple\" socially meaningful groups (e.g., skin tone in skin lesion) the same way you could have very complex “domains\" (e.g., geographic location of a sample) so I don't believe there is a hard difference for the mitigation algorithms. Furthermore, I don't think the authors can argue about using socially meaningful groups when they are using very un-meaningful classification targets for some of their experiments (e.g., whether someone has wavy hair in CelebA)!. \n\nW4: I generally think there are a lot of tables that are hard to interpret. Many also do not have boldings or standard deviations and inconsistent numbers of significant decimal places. This gives the manuscript an unfinished aspect. I would recommend that the authors try to make some more of the tables into plots (for instance Table 15 on LLM size). I would also recommend that they vary the interpretations, for example when you say that optimizer choice matters, instead of just listing all the different metric values for different datasets and optimiser it would be helpful to include some summary stats, e.g., overall accuracy varies by a max of this much, or accuracy gap can increase by 20%.\n\nW5: It would be interesting for the authors to check other LLM tasks like fairness of generated outputs. \n\nW6: In fig3 it would be helpful to show overall accuracy as well because we don’t know whether the models are harming overall performance or not. The authors could also include some kind of statistical testing to see if there are any significant differences relative to ERM. \n\nW7: One of your datasets fairface has no gap (0.86)! What’s the point of including it in the fairness analysis?\n\nW8: Small concern that gender and race classification tasks may be problematic.\n\nW9: There are lots of missing details on the way the mitigation methods work, even in the appendix. This is particularly true for bias mimicking, FIS, DFR (should specify that you are retraining the last layer on a balanced distribution! - right?), CLIP/BLIP models and for related post-training debiasing methods!\n\nW10: It would be helpful to show variance of results for each method across hyper-parameters.\n\n**Minor weaknesses**\n\nW11: Min max fairness definition shouldn't have the min as you are presenting the metrics not the way they're optimised (to keep it consistent with the other fairness definitions). \n\nW12: All the left quotes are wrong. Please use `` for left quotes and '' for right quotes in LaTeX!\n\nW13: Table 1 columns are a bit confusing. I would just put the proportion of the targets.\n\nW14: Colouring the cells in table 3 to give an indication of better worse metrics would be helpful for the reader.\n\nW15: The wording in the conclusion “the fairness issue remains unresolved.\" is overly simplified and referring to \"the fairness issue\" feels imprecise, as fairness is so multi-faceted.\n\nW16: What does this wording mean in section B1 “Without additional illustration”?\n\nW17: It would be helpful to add a note on which methods use demographic information.\n\nW18: Undefined reference line 1114.\n\nW19: Table 15: you can also get an SD for the average metrics.\n\n[1] Rethinking Fair Representation Learning for Performance-Sensitive Tasks, ICLR 2024.\n[2] Change is Hard: A Closer Look at Subpopulation Shift, ICML 2023.\n[3] Mind the Graph When Balancing Data for Fairness or Robustness, NeurIPS 2024.\n[4] Automatic dataset shift identification to support safe deployment of medical imaging AI, MICCAI 2025.\n[5] Critical Appraisal of Fairness Metrics in Clinical Predictive AI, arXiv 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OXvx6Ins9s", "reviewer": "Reviewer_ZcM2", "summary": "This paper introduces NH-Fair, a benchmark for \"Fairness Without Harm\" (FWH). Its core contribution is demonstrating that a rigorously tuned ERM baseline (using a novel DTO/FWH selection) often outperforms complex bias mitigation methods. The benchmark is extended to LVLMs, finding they still suffer from bias and that scaling does not automatically fix it.", "questions": "1.LVLM Fine-tuning: How do you expect your zero-shot LVLM findings to generalize to the more common fine-tuning setting?\n\n2.HPO Cost: Your finding relies on 10k+ GPU-hours. What is the \"budget-friendly\" HPO recommendation for a practitioner who cannot afford this?\n\n3.Waterbirds: You note Waterbirds is a spurious correlation dataset, not a social bias one. Why include it in a social fairness benchmark if it's \"easier to resolve\" and may dilute the main message?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.Practical Problem: The \"Fairness Without Harm\" (FWH) principle is highly relevant for real-world deployment (e.g., healthcare).\n\n2.Strong Baseline: The paper's key finding—that a well-tuned ERM can beat specialized algorithms—is a crucial, critical finding for the field, backed by extensive HPO.\n\n3.Rigorous Protocol: The DTO/FWH model selection strategy provides a novel and fair method for comparing models.\n\n4.Timely LVLM Analysis: The paper correctly identifies that LVLMs are not a panacea for fairness, a critical and timely observation.", "weaknesses": "1.Limited LVLM Scope: The LVLM evaluation is zero-shot only. This is a major gap, as models are typically fine-tuned, a process which could significantly alter fairness outcomes.\n\n2.No Intersectional Analysis: The benchmark only considers single sensitive attributes, ignoring intersectional biases (e.g., race and gender), which can be more severe.\n\n3.Lacks Deep Insight: The paper is excellent at observing (e.g., \"RandAug works well\" , \"Optimizers matter\" ), but provides little analysis as to why these phenomena occur.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YjzyvWyY65", "reviewer": "Reviewer_dLUc", "summary": "In this paper, the authors created a big standardized benchmark called NH-fair. The main idea is to be able to compare debiasing methods against an ERM baseline. So they first tunned the base model (ERM) exhaustively using a DTO selection strategy, for over 10,000 A100 GPU hours, which proved that optimizer choice and pretrained wiehgts are crucial, fixing these choices leads to unfair comparisons. The results show that most specialized debiasing algorithms do not realiby outperform this ERM baseline. Instead, simple data augmentation (like RandAug) often achieves \"Fairness without harm\"(FWH), improving fairness without sacrificing utility. From their analysis, they revealed that huge LVLMs still exhibit significant subgroup disparities and simply scaling model size doesnt guarantee fairness.", "questions": "Data Augmentation is claimed to emerge as a promising practical strategy that \"most often achieves fairness without utility loss\" . Given that the FWH methodology allows for model selection in either the Optimal Zone or the Sub-Optimal Zone, could you quantify, across all seven datasets, the success rate of a method like RandAugment when classifying its best-found model into the Optimal Zone versus the Sub-Optimal Zone?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "I think the paper has a good originality as the NH-Fair unifies bias mitigation evaluation across tradicional supervised vision models and also LVLMs. It has a big significance as they introduce the FWH methodology. They did a big execution, solving the idea of insufficiently trained baseline problem as they spent over 10,000 A100 GPU hours with exhaustive hyperparameter optimization.", "weaknesses": "The paper skips theoretical fairness concepts, as they only focus on group parity metrics, omitting individual fairness and counterfactual fairness.\nFuture work should try to incorporate these for a wider view of bias.\nMost part of data was about human faces, for future work needs to include more visual domains.\nThey did not fully use all sensitive attributes for evaluate intersectional groups (like gender vs race).\nLVLMs were evaluated only with zero shot prediction mode (classification) even the paper suggesting finetuning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)