FairerCLIP: Debiasing CLIP's Zero-Shot Predictions using Functions in RKHSs
Authors: Sepehr Dehdashtian
Lan Wang
Vishnu Boddeti
Venue: ICLR 2024 poster (2024)
Keywords: CLIP
debiasing
fair representation learning
vision-language models
dependence measure
zero-shot
Primary area: representation learning for computer vision, audio, language, and other modalities

Abstract: Large pre-trained vision-language models such as CLIP provide compact and general-purpose representations of text and images that are demonstrably effective across multiple downstream zero-shot prediction tasks. However, owing to the nature of their training process, these models have the potential to 1) propagate or amplify societal biases in the training data and 2) learn to rely on spurious features. This paper proposes FairerCLIP, a general approach for making zero-shot predictions of CLIP more fair and robust to spurious correlations. We formulate the problem of jointly debiasing CLIP’s image and text representations in reproducing kernel Hilbert spaces (RKHSs), which affords multiple benefits: 1) Flexibility: Unlike existing approaches, which are specialized to either learn with or without ground-truth labels, FairerCLIP is adaptable to learning in both scenarios. 2) Ease of Optimization: FairerCLIP lends itself to an iterative optimization involving closed-form solvers, which leads to 4×-10× faster training than the existing methods. 3) Sample Efficiency: Under sample-limited conditions, FairerCLIP significantly outperforms baselines when they fail entirely. And, 4) Performance: Empirically, FairerCLIP achieves appreciable accuracy gains on benchmark fairness and spurious correlation datasets over their respective baselines.

Published as a conference paper at ICLR 2024
FAIRERCLIP: DEBIASING CLIP's ZERO-SHOT PREDIC- TIONS USING FUNCTIONS IN RKHSs
Sepehr Dehdashtian" Lan Wang"
Vishnu Naresh Boddeti
Michigan State University
(sepehr, wanglan3, vi vishnu @msu. edu
ABSTRACT
Large pre-trained dision-languee models such CLIP provide compact and general-purpose representations of text and images that are demonstrably effective across multiple downstream zero-shot prediction tasks. However, owing the nature their training process, these models have the potential to 1) propagate or amplify socictal biases in the training data and 2) learn rely on spurious features. This paper proposes FairerCLIP, general approach for making zero-shot predictions of CLIP more fair and robust to spurious correlations. We formulate the problem of jointly debiasing CLIP's image and text representations in reproducing kernel Hilbert spaces (RKHSs), which affords multiple benefits: Flexibility: Unlike existing approaches, which are specialized to either learn with or without ground-truth labels, FairerCLIP is adaptable to learning
both scenarios. 2) FairerCLIP lends itself to an iterative optimization involv- ing closed-form solvers, which leads to 4x-10x faster training than the existing methods. 3) Sample Efficiency: Under sample-limited conditions, FairerCIIIP significantly outperforms baselines when they fail entirely. And, 4) Performance: Empirically, FairerCLIP achieves appreciable accuracy gains on benchmark fair- ness and spurious correlation datasets over their respective baselines.
Ease of Optimization:
INTRODUCTION
Vision-Language Models such as CLIP (Radford al. 2021) are trained on large-scale datasets of image-text pairs to learn representations that have high similarity for related image-text pairs. While these models have gained significant attention in recent years due to their remarkable zero-shot classification capabilities, they are not flawless. There is growing evidence that such models suffer from biases w.r.t. demographic (e.g., sex or skin tone) attributes (Agarwal et al. [021 Wang et al..
2021 Birhane et al. 2023a b) Dehdashtian et al. (024) and even non-demographicce. image
background or illumination) attributes (Du et al. 2022 Zhang & Ré 2022).
The above-mentioned biases can be viewed based on de- pendencies between the data attributes. We show these dependencies in Fig. X is the data (e.g., face images) that depends on some attributes, including Y, the target attribute wish predict, and S, the attribute that leads to bias. The goal of bias mitigation is to ensure that the prediction is independent of S. We group the biases into those arising from two scenarios: (1) Y (Fig.Ta). and S are dependent (Fig. a):: for example, high cheekbones as and sex as S since males typically have higher checkbones than females. We refer to this type of correlation as intrinsic dependence. independent (Fig. b): for example hair color as Y and sex as since the hair
(a)
(b)
Figure 1: Dependence graphs for debiasing.
(2) Y and color of a person does not depend on their sex. In this case, we refer to any observed correlation as spurious correlation.
Several efforts Zhhag & Ré| 2022, Gao et al.) 2021 Kumar et al.) 2022 Kirichenko et al. 2022 Chuang et al. |023] Wortsman et al. 2022 An et al. 2023 Adila et al. 2023). have been made to debias zero-shot predictions from CLIP models. However, they are limited in either one more respects: (1) Type of Bias: Existing CLIP debiasing methods only consider spurious correlations
"Equal Contribution.

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Published as a conference paper at ICLR 2024
Awat Tht
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Coere Simiaaiy Chediier
f/(. (p..,rr
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(a) Pre-Train
(b) Train
(c) Inference
Figure 2: Overview of the train and inference phases of FairerCLIP. (a) Shows the label prediction step. When labels are not available for training, FairerCIIIP uses cosine similarity between the X and X,, and XTs and X, to predict the target attributes and sensitive attributes, respectively. (b) Shows the inputs and outputs for FairerCLIP in its training stage. FairerCLIP uses representation of images and the corresponding text prompts that are constructed by target attribute (Y) along with the predicted labels to find the image and text encoders, i.e., fj (-: θ₂) and fi Or). (c) Shows the inference phase of FairerCLIP in which we use the trained image and text encoders to generate debiased representations from the ones generated by CLIP.
in the data (Fig. Tb) and do not seek to address bias induced by pairs of attributes with intrinsic dependencies (Fig. a). (2) Labels for training: All existing approaches are tailored to train/fine- tune either with (supervised) or without (unsupervised) ground-truth labels and, as such, cannot be employed in both scenarios, (3) Efficiency: Some approaches adopt iterative methods to debias the features. However, they are computationally expensive train, i.e., slow to converge, leading to high training latency and many parameters in the debiasing modules, increasing model sizes even further.
We propose FairerCLIP to address the aforementioned limitations of existing debiasing approaches. FairerCLIP affords sufficient flcxibility to mitigate bias arising from both spurious correlations and intrinsic dependencies and, in both scenarios, learn with without ground-truth labels. FairerCLIP utilizes a non-parane -orramamettiic measure of statistical dependence that accounts for all linear and non- linear relations between the debiased representation and the sensitive attribute of interest. Our formulation lends itself to alternating optimization, with each update having a closed-fone solution and. in comparison baselines, enjoying fast training convergence and requiring fewer parameters to train. An overview of FairerCLIP in its train and inference phases along with how we integrate this transformation over the underlying CLIP model is shown in Fig.
Summary of Contributions: (1) We demonstrate that single general method can debias the image and text features from frozen CLIP backbones under different scenarios more effectively than those specialized for each scenario. The scenarios include accounting for both spurious correlations and intrinsic dependencies (Sec. 4.2), learning with and without ground-truth labels (Sec. 4..2), and learning from small and medium-sized datasets (App. AA5). (2) We demonstrate that kernel methods are particularly effective compared to shallow MLPs when operating on features and optimizing possibly competing objectives, as is the for debiasing CLIP representations. They enjoy closed- form solutions that allow for significantly faster training, can scale to medium-sized datasets, and are more effective under limited training data (Sec. 4.3 App. A.4 and Tab. 3ab.33 (lett).
THE DEBIASING CLIP REPRESENTATIONS PROBLEM
Scalars are denoted by regular lower case letters, e.g. r, T. Deterministic vectors are denoted by boldface lowercase letters, e.g., s. We denote both scalar-valued and Random Variables (RVs) by regular uppercase letters, e.g. X, S. Deterministic matrices are denoted by boldface uppercase letters, e.g. H, O and the entry at ith row, jth column of matrix M is denoted by ( (M);, or my.. I, or simply I denotes an n x n identity matrix, 1, or and O,, or are n X 1 vector of ones and zeros, respectively. We denote the trace of any square matrix K by Tr K]. Finite or infinite sets denoted by calligraphy letters, e.g., H. A.
Notation:
Problem Setup: We assume that the joint RV (X,,Xr, Y, S) contains the pre-trained image features X, E ddd pre-trained text features of target attribute Xyr Rdd target attribute Y Rdy and
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sensitive attribute S E Rds. Their joint distribution will be YY,, Furthermore, Y and S can also belong to any finite set, such as a categorical set.
Our aim is to debias X1 and Xr by generating representations, Z = (Xr) and ZT fX with no or reduced statistical dependence on S. To measure this dependency, we need to employ a metric capable of capturing both linear and non-linear statistical dependencies.
Choice of Dependence Measure: We will adopt simplified definition of the Hilbert-Schmine Independence Criterion (HSIC) (Gretton et al.
2005) introduced by /Sadeehii et al. (2022). defined as,
Dep(Z.S) = Σ Σ
Cov2 (())
j=1 BEl4s
where Ug is a countable orthonormal basis set for the separable universal RKHS Hs. and Z; (X) for 1 E HxVj = r. Dep(Z, S) can be estimated (see Lemma 1 in Sadeghi et al. (2022) empirically as,
1 HsK n22
Dep(Z,
(2)
where n is the number of data samples, Kx E R=*x is the Gram matrix corresponding to Hx. O
the encoder parameter in f(X) = |xx; "x,,,". ... kx.]² H H= I, 1 1, ,,,,, 1, is the centering matrix, and Lg is full column-rank matrix corresponding to the Cholesky factorization of Ks. i.e.
L Ks. While HSIC and related measures like Maximum Mean Discrepancy (MMD) Gretton
2012) have been employed by prior fairness approaches (Bahng et al. 2020; Quadrianto et al. Jung et al. 202I), the HSIC variation we use in Eq. 2 has several attractive properties (Sadeghi practical ability to capture all non-linear modes of dependencies when projecting from high-dimensional representation to low-dimensional representation, and, as we demonstrate next, analytical tractability.
al.
2019
ct al.
2022). This includes a convergence rate of On 1/2 (nn11/2/
In addition the meetiomeet dependence metric, we also need another metric that can mimic the cosine similarity-baa classification employed by CLIP. This metric will be used to make the representations of images and their corresponding text prompts align with each other to improve the accuracy of the predictions. As a result, we modify the definition of Dep metric in Eq. ( and use a linear kernel as B in Lemma [
Lemma Let KX Kxr [Rax be the Gram matrices corresponding to Hx, and Hxp, re- spectively, i.e., (Kx,);s kx, (I, and (Kxr)s = kx, (2T, (T,,, where covariance is empirically estimated as
Cov (X1). 9m (XT))
1 n
Σ
(01, )9m (TT:))
f0(
Im (xTT.)
n² p=1
follows that, the corresponding empirical estimator for Dep (Z1, ZT) is Dep [7r, 7r) = n Kx HKx, x, θ
(3)
where 2 and O are the parameters of the image and text encoders, respectively, and Kx, and K xx the kernel matrices for the image and text features, respectively.
Proof. The main idea for proving equality equation iis to employ the representer theorem to express fy and Im- The complete proof is available in the supplementary material.
Objective Function: After choosing the appropriate dependence measure, we now define our objective function. Our goal to mitigate bias in CLIP'' zero-shot predictions by debiasing the underlying representations. This can be achieved by (1) reducing the information related to the sensitive attribute while (2) preserving information about the target attribute as much as possible the pair of image-text representations and (3) keeping the image and corresponding text representations aligned with each other.
We formulate the
learning objective through the following optimization problem.
In scenarios where only a single or few samples are available, to an extent, heavy data augmentation can compensate for the lack of sufficient samples accurately estimate Dep. However, this is beyond the scope of this paper, and all experiments are performed without data augmentation.
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Definition 1.
sup frEA
= Dep - DyDep (2r,))
+ Dep (T, Y) Tr-eep (Zr,
(4)
+ Tep (Zi, Zr)
where Dep(:.)) O is the statistical dependence
defined in Eq. 22. Qep(Q.U) 00 implies Q is independent of (i.e., Q LL U). and Dep(Q, U) > implies ? is dependent on U (i.e., Q X1 U); with larger values indicating greater degrees of dependence. тл and Ty control the contribution of the corresponding debiasing terms and T= controls the alignment of the debiased image and text features Z₁ = (X1 and ZT fx respectively.
In the above definition, the terms Dep(Z,, Y) and Dep( ZT, Y) contribute maximizing the statistical dependence between the representations and the target label Y, the terms Dep 2,, S) and -Ty Dep( Zr.S) S) seek to make the representations independent of S, and the term TΣ TyDep( Z1, Zr) ensures that the text and image features are still aligned with each other after debiasing.
Choice Encoder: We construct the mappings through a of functions from IRx in reproducing kernel Hilbert space (RKHS) (H.x, (x(,,-)), such as the RBF Gaussian kernel. Hence, the representation can be expressed as,
= f() = Kz, 2.1² ER, 2n fX),, E Hx Vj I....,
(5)
where r the dimensionality of the transformed representation.
Our choice of RKHS is motivated by several reasons. As observe in Eq. debiasing is inherently an optimization problem with multiple competing objectives. In such cases, optimization is the primary bottleneck rather than model expressivity. This was also observed in [Sadeghi et al. (2022). The closed-form solution afforded by our approach mitigates the optimization challenges (Sec. 4.3 and App. A. RKHS has nice universal approximation properties and has performance comparable to shallow MLPs while being more computationally efficient for training (Sec. 4.3) and performant under limited data scenarios (Sec. 4.2
A SOLUTION TO THE DEBIASING CLIP REPRESENTATIONS PROBLEM
Given the choice of dependence measure in Eq. the optimization problem in Eq. expressed as,
can be
max JrE.
X,
HLy 11: 1
HLs
n
n
n² HLy
Or x
Tr
Kx,
H
n2
O, HK x O
Tz
(6)
Our solution to the constrained optimization problem in Eq. (6) is based on the observation that it has closed-form solution when either fi fr are fixed. Let Zo be the feature corresponding to the fixed parameter and f the optimization parameter of the other feature of interest. Then Eq. (6)
reduces to two optimization problems of the following general form,
1 n
1 |O
max fEA,
10xxHLy
10 HL
HZ
(7)
This is easy since fixing either of the parameters in Eq. (6) renders the terms involving them a constant w.r.t. the variable of interest, and hence can be ignored during optimization.
Theorem 2. Let the Cholesky factorization of Kx be Kx Lx where Lx E RRudd (d S n)
a full column-ran matrix. Let < then solution Eq. 47
popt (X) opt [Axx (201, X), Ax (2 X))²
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FairerCLIP
Noa trinable
Trainabee
X1
Mappon
Choual-Fiem
Z,
Repecsetration Space
CLP
Cosine Simvlanty Classlder
"Thir ploto celebiity cebebiity with high chankhomes"
(X
"Thir in plown celobiity with chrekhhner
Ar
Teat Mayper
Uimodt Ferm Selver
w/ labels: Y who labels:
Zy
fe5
Figure FairerCLIP acts on representations extracted from a frozen CLIP model. It has two mapping functions, fr and fr, for the image and text representations. These functions are learned through an alternating optimization algorithm with two closed-form solvers. When ground-truth labels are unavailable for training, FairerCIIIP learns from pseudo-labels ý, which are initialized from CLIP's zero-shot predictions and refined iteratively. The bold words in the input text prompts are the information of the target task label included in the text prompts.
where 9p L and the columns of U are eigenvectors corresponding to the largest eigenvalues of the following generalized eigenvalue problem.
L - HZZZZZHH Lxu= 1 (iixx+7]]
(8)
Furthermore, the objective value of is equal to Ej=1 Xo. where (i,,". .. .dr are r largest cigenvalues of Theorem 2
Proof. The objective in Eq. ( can be expressed trace optimization problem, which reduces to a generalized eigenvalue problem (Kokiopoulou et al. (001]). See the supplementary material for detailed proof.
Building upon the above closed-form solution, we adopt alternating optimization to solve Eq. (6).
by fixing fi and solving for fr and vice-versa (Fig. 3) The formulation in Eq. 44) requires labels of the downstream target task Y and the sensitive labels to learn FairerCL.IP's parameters. While such labels readily available for supervised learning and partially available for semi-supervised learning (Jung et al.) 2022] Chen et al. (2023), this not the case for unsupervised learning. Therefore, in this case, we initialize the labels Y and S by the original zero-shot predictions Ý and Š from CLIP (Fig. a). Then we refine Y by predicting it after every iterative update of fı and fr. However, note that we do not update in the same way since our initial prediction of Š has the most information about label S, but as we debias representations in the subsequent iterations, we remove information of S. Therefore, updated values of Š will lead to inaccurate estimates of Dep(Z, S) and affect the overall optimization. This procedure is detailed in Algorithm of App. A.I
3.1 A GEOMETRIC ILLUSTRATION OF FAIRERCLIP
geometric illustration of the steps that FairerCLIP takes debias the representations is shown in Fig. 4 In theory, the RBF kemels used in our encoder (ox(X) and (r())) map the image and text features into an infiniteddimensional space, where the samples corresponding to different target attributes are linearly separable. In the infiniteddimensional space, the encoder that optimizes Eq. (4) for O and Or by alternating between closed-form solvers and seeks a direction for mapping the image and text features that have low angular distance w.r.t. the direction of (1) Y labels (small azy and ogpy) ) (2) S1 (small ass, and ars ), and (3) the other representation (small ayr).
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(I(x)
0m
Prs.
cry
dr (x))
Figure 4: A geometric illustration of FairerCLIP training steps. The encoder utilizes the implicit mapping functions (X) and (x of the RBF kernel map image and text features into an infinite-dimensionall space, facilitating linear separability of samples with different target attributes. The optimization process seeks direction that aligns with labels Y, statistically independent of S, and aligned with the other representation.
EXPERIMENTAL EVALUATION
We evaluate FairerCLIP on datasets with spurious correlation and intrinsic dependence and compare it to several existing baselines. In summary, the experimental results indicate that the baseline methods effective in mitigating spurious correlations. but they not effective at mitigating the bias caused by the intrinsic dependencies. In contrast, FairerCLIP effectively and efficiently mitigates both spurious correlations and intrinsic dependencies in CLIP's zero-shot predictions. In all experiments, to overcome the (nn²) computational and Onn²) memory complexity of the kernel matrices K, details are provided in App. A.3
random Fourier features (RFF) (Rahimi & Recht 2007). All the implementation
4.1 DATASETS
We evaluate FairerCLIP on an assortment of classification tasks across many datasets. This includes Waterbirds (Sagawa et al. 2019), which contains spurious correlations between the types of birds and background of the images, different settings of CelebA (Liu et al., 2015) that contains more than 200,000 face images of the celebrities in the wild annotated with 40 binary attributes and contains both spurious correlations and intrinsic dependencies among its attributes, FairFace dataset (Krkkainen & Joo (021) which contains more than 108,000 face images from 7 different race groups (White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino) collected from the YFCC-100M Flickr dataset and labeled with race, sex, and age groups, and Chicago Face Database (CFD) (Ma al. 2015) which includes face images with different annotations such as facial attributes, ethnicity, age, and sex.
4.2 EMPIRICAL RESULTS
We report the results of FairerCLIP and compare them with the performance of related baselines on various datasets and settings. Following the experimental settings of prior work (Zhang & R 2022] Koh et al. 2021 Chuang et al. 2023). we do not presume sensitive attributes (S) during the training process but assume them in the validation dataset for hyperparameter tuning and model selection,
as proposed in Koh et al. (2021). Thus, following prior work (Zhang & Ré 2022), for FairerCLIP and other baselines that need label S, we use the zero-shot predictions of S (S) from CLIP as the sensitive attribute.
4.2. MITIGATING INTRINSIC DEPENDENCY
To evaluate the ability FairerCLIP to mitigate intrinsic dependency, we conduct numerical eval- uations the CelebA dataset with high cheekhones as the target attribute and sex as the sensitive

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attribute. As diseussed in Sec. I these two attributes are intrinsically dependent. To measure the fairness of predictions, we employ the Equal Opportunity Difference (EOD) (Hardt et al. 2016)
metric, defined as, EOD := P( = 1|Y = = =1) 1) = 1|Y = 1,5 0) where S is the
sensitive attribute, and Y and Y are the predicted and the ground-truth target labels, respectively. Our choice of EOD justified since other fairness definitions, like Demographic Parity Violation (DPV), are not well suited for many practical scenarios (Hardt et al.) 2016; Chouldechova 2017).
Table compares the performance of FairerCLIP and the baselines on the CelebA dataset with intrinsic de- pendeney. For this experiment, we train all methods except the zero-shot baseline, which is included to demonstrate the level of unfairness in the CLIP fea-
Table 1: Fairnsss on the CelehA dataset with intrinsic dependency. All values in %.
ResNer-SU
VIT-U/I4
Mathod
Arg
BOD
Ang
BDD
Zero-het
50.5
5.8
48.8
288
tures with the ground-truth labels. We observe that among all baselines, Contrastive Adapter (Zhang & Ré 2022) performs well and achieves appreciable FOD for the CLIP ResNet-50 model. However, most other methods seem even amplify the bias in the original CLIP features while improving average accuracy. FairerCLIP performs the best in terms of debiasing, achieving an EOD of 0.002% and 0.005% for CLIP ResNet-50 and CLIP ViT-L/14, respectively. Overall, FairerCLIP very effective at mitigating unfairness to a significant extent, achieving an EOD value close to zero while maintaining a high classification accuracy.
FRM inctrvmn
84.8
19.0
84.8
14.0
ERM Adapler
85.3
11.0
34.6
14.0
DFR (Suhomm
83.2
4.2
34.1
7.4
DPR (Upeamples
K1.6
4.1
36.
6.6
Contassive Adsplr
84.2
1.0
85.6
6.3
FamreC1.P 1.I (oun)
81.4
0
85.8
0,088
4.2.2 MITIGATING SPURIOUS CORRELATION
To evaluate FairerCLIP's effectiveness in mitigating spurious correlation, we perform numerical experiments on spurious correlation benchmarks, Waterbirds and CelebA, following the settings in Zhang Rē 2022). Since FairerCL.IP can be learned with or without ground-truth labels, we compare it against methods from these two categories. For performance evaluation, we use three metrics: 1) Average accuracy (Avg.). 2) Worst-Group accuracy (WG), i... the lowest accuracy of all subgroups, and 3) Gap, which is the difference between average and worst-group accuracy.
Table 2: Comparison of prior methods and FairerCLIP in terms of worst group accuracy (WG), average accuracy (Avg), and their gap on the WaterBirds and CelebA datasets. For the latter, the target and sensitive attributes are blonde hair and sex for two different variants of CLIP, CLIP ResNet-50 and CLIP ViT-L/14, in two different settings-w/ and w/o labels. 1st / 2nd best results are in bold/ / underlined.
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Tab sshows the results of our empirical evaluation. We make the following observations: (i) On CLIP ViT-L/14, FairerCLIP has the lowest Gap and highest WG accuracy. (ii) For the CLIP ResNet-50, FairerCLIP outperforms the baselines in the w/o labels setting but not in the w/ label setting. The discrepancy between the performance FairerCLIP with CLIP ViT-L/14 and CLIP ResNet-50 can be attributed the fact that CLIP ResNet-50 features contain less information about target attributes than CLIP ViT-L/14 features, as shown in App. A.7 Overall, the results of Tab. 2 Lindicate that FairerCLIP effectively improves the worst group's 's accuracy and reduces the Gap. Notably, our approach can be applied to and is effective in both scenarios, with and without ground-truth labels. At the same time, the baselines are specialized to operate in one or the other scenario only.
In Tab. 3. we evaluate FairerCLIP on the FairFace dataset. Here, we consider sex and race as the sensi- tive attributes and follow the experimental setup in Chuang et al. (2023). We use five target attributes and ten text prompts (2 prompts per attribute) unrelated to the samples' facial or sensitive attributes; do not have access to ground-trure labels. As an example, the text prompt can be "A photo of a crimi- nal person" " or "A photo of a friendly person". All the ten specific prompts are in App. A.3 To evalu-
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ate the models, calculate MaxSkew @ 1000 (Geyik et al. 2019). which assesses the maximum im- balance in certain sensitive attributes within a dataset. As is shown in Tab. FairerCLIP outperforms
the other baselines for both sensitive attributes across two different CLIP backbones.
Table 3: Comparison of FairerCLIP with base- lines on FairFace dataset.
In summary, the results in Tab. 2 Tab. 3 and Tab. [ suggest that FairerCLIP can effectively mitigate the
CLIP VIT-B/32
CLIP VIT-L/14
Method/ MaxSkew
Sex
Sex
demographic bias caused by spurious correlation and
7ern-Shoe bwkete
0.206
0.743
0.206
0.768
Orth-Pro
0.146
0.755
0.349
0.605
intrinsic dependency in the data in both w/ and w/o
Orth Cali
0.102
0.638
0.200
0.461
the ground -truth labels settings.
Fairer Tou3s
0.097
0.099
Next, we consider a more challenging task to evaluate the data- efficiency of FairerCLIP. We use CFD images with attractive and sex as the target and sensitive group attributes. The former continuous label, which we binarize by using the mean value of all samples as a threshoid. Moreover, the sex attribute is a binary label. This task presents challenges in two aspects. First, the number of samples in this dataset is very small (597 samples), which may not be sufficient for training some of the baselines. Second, the performance of the zero-shot classifier for this case shows that the features generated by the CLIPP model are not well separated, rendering it difficult to correctly predict S (see Appendix A.8).. Fig. 5 shows the results of this experiment. We first observe that all the baselines almost completely fail at mitigating the bias for the worst group. In contrast, FairerCLIP's performance is satisfyingly better, both in terms of the worst group (WG) and average (Avg) accuracy. Furthermore, the Gap significantly lower (21.8% vs 33.7% for (Zhang & Ré. 2022
Aor
66
FIR Adeeun Siaaeral piaT
Aulitte
FatrerCLIIP our))
WG (%)
Figure 5: Results of FairerCLIP and basclines on CFD
4.3 COMPUTATIONAL EFFICIENCY OF TRAINING
To show the computational efficiency of FairerCLIP we report and compare the training time of Fair- erCLIP and other baselines in Tab. 4 The results show that FairerCLIP is an order of magnitude faster than most baselines and almost two orders faster than Contrastive Adapter (Zhang & R 2022). The un- derlying model for this experiment is CLIP ViT-L/14, and all the numbers are measured on the same machine.
Table 4: Training time comparison (in seconds).
Method
Waterbirds
ColchA
Contrastive Adipter & Rd 2022
1200
20602
ERM Lineur Probe
157
2437
Adapter Capce
161
1924
(Suhiampie Ka
1878
DFR (Upamppe)) Kirkkiiki 24
176
2662
FairerCLIP (ours)
32
5 ABLATION STUDIES
We conduct systematic ablation studies in different settings to investigate the effectiveness of indi- vidual components of our approach. The settings include spurious correlation, intrinsic dependency experiments, and scenarios where ground-truth labels are unavailable. The results are shown in Tab. S where Tab, (left) shows results of training w/ labels and Tab. (right) shows training w/o labels. In the following, we describe each of these studies. For more ablation studies please refer App. A.6
Effect of Dep( Z, Y) term: Here we study the effect of Dep(Z, Y' by only retaining Dep Z, Y) in the objective. In this case, the worst group accuracy dropped by 14.5% for the group robustness experiment, and the EO increased to 0.195% for the fairness experiment. Although the new features were still able to maintain good separation w.r.t. Y, they lost their debiasing ability to a large extent.
Effect of Dep( Zr, ZT) term: We remove Dep(2r. Zr, ZT) to investigate its effect on the alignment between the image and text embeddings. Results show that while maintaining a similar worst group accuracy, there is decrease in average accuracy for both under the w/ and w/o ground-truth setups. This result demonstrates the contribution of this component in improving the predictions. Similarly, the results for the fairness experiment show that Dep Z,, Zr) also aids in enhancing the debiasing ability of FairerCLIP.
Effect of updating Y: In this experiment, we predict y once and fix it through the training process. Updating it during the training iterations has considerable impact on worst group accuracy. The
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Table 5: Ablation study w/ (left) and w/o (right) ground-truth labels the CelebA dataset for different target attributes with sex as the sensitive attribute. We compare the effect of different components and parameters of FairerCLIP on its performance. Both Dep( 1 Zr) and Dep (2, Y) prove to be necessary and effective in maximizing the metrics. All values are in %.
Blonde Hair
High checkbones
Blonde Hair
Method
WG
Avg
Gap
Avg
EOD
Method
Avg
Gup
Depí Z. only (F
75.0
87.7
12.7
Depí Y) only (T 0)
72.2
89.8
17.6
83.8
6.4
Dep(2,, Zr) (F. 0)
86.1
w/o Dep( 21, 2r) (r. = 0)
87.0
88.7
1.7
83,8
0.2
w/o updating 0
81.1
87.3
6.2
FairerCIIIP
86.7
89.3
2.6
83.8
0.005
FaircrCLIP
86.1
88.8
2.7
initial zero-shot accuracy for this group was 72.8%. Using the same initial Y during training improves the accuracy 81.1% while updating y while training improves it further to 86. 1..
6 RELATED WORK
CLIP and Bias: Recent advancements in CLIP like models utilize multimodal data to learn represen- tations that demonstrably generalize well to many downstream tasks and associated datasets (Radford
itt 2021 Desai & Johnson [202]] Singh et al. [022] Zellers et al. [021]] Zhang et al., 1021
Alayrac et al. 2022). Radford et al. (2021) demonstrated that utilizing a simple pretraining task with massive amounts of image-text pairs collected from the Internet, lead to models with strong transferability on different downstream tasks. FLAAVA (Singh et al., 2022) learns representations by jointly pretraining on both unimodal and multimodal data. Flamingo Alayrac et al. (2022) demon- strated excellent generalization performance in few-shot and zero-shot scenarios. However, growing evidence (Birhane al., [2023a] b) shows these models suffer from spurious correlations and bias towards certain demographic groups. For instance, Chuang et al. (2023) showed that textual prompt embeddings capture spurious correlations. In addition. Agarwal et al. (02T)) discovered that zero- shot prediction from CLIP representations showed a high misclassification rate for certain races. Similarly. Wolfe al. (2022) observed that CLIP embeddings exhibit stereotypes about and race. Dehdashtian et al. (2024) numerically characterize two near-optimal accuracy- -airres-fairness tradc-offs and evaluate how far CLIP models are from them. Complementing these observations, we observe that CLIP exhibits high levels of demographic bias on the CFD and CelebA datasets.
Several approaches have been proposed to debias CLIP embeddings. Wang et al. (222)) addressed bias in image search by combining balanced sampling and pruning spuriously correlated embeddings. Waan et al. (2022) proposed a two-stage method that used learnable word vector prefixes and a re-representation Matrix for debiasing image retrieval problems. |Berg et al. (2022) jointly trained an adversarial classifier and image-text contrastive loss, effectively reducing different bias measures. Zhang & Ré (2022) employed contrastive adapter training strategy to enhance group robustness. Following the group robustness evaluation, Citiuaatoo,,Cuuanggg et al. (2023) proposed to remove bias from text embeddings by projecting out the biased direction with text data only. Seth et al.] (2023) adapted an additive residual learner module that separates the protected attribute information from the image representation generated by the visual encoder of CLIP.
Debiasing CLIP:
CONCLUDING REMARKS
This paper proposed FairerCLIP to mitigate bias in zero-shot predictions from CLIP. It is versatile enough to mitigate bias caused by both spurious correlations and intrinsic dependencies in data and can be trained with or without ground-trutt labels. Our key idea was to model the CLIP debiasing problem in reproducing kernel Hilbert spaces and employ parameeriic statistical dependence measure that considers all linear and non-linear relations between the representation and the attribute of interest. Our solution the form of an alternating optimization algorithm is effective across a diverse set of datasets, including Waterbirds, CelebA, FairFace, and the Chicago Face Database, spanning a variety of intrinsic dependencies and spurious correlations among attributes. Lastly, kernel-based approaches are underrepresented in current learning solutions, and FairerCLIP shows their strong potential for the type of task considered in this paper due to its flexibility, ease of optimization, and promising performance.
Acknowledgements: This work was supported in part by the National Science Foundation (award #21471161 16) and the Office of Naval Research (award #N00014-23-1-24177

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A APPENDIX
In our main paper, we proposed FairerCLIP to debias the text and image features from pre-trained vision-languee models. Here, we provide some additional analysis support our main results. The supplementary material is structured as follows:
1. Representation Disentanglement and Training Algorithm in App. A.1
2. Proofs Lemmas and Theorems App. A.2
3. Implementation details in App. A.3
4. Analysis of analytical computational complexity and memory complexity in App. A.4
5. Effect of data size on the performance of FairerCLIP in App. A.5
6. More ablation studies in App. A.6
7. Comparison of CLIP ViT-L/14 and CLIP ResNet-50 in App. A.7
8. Comparison of more than 100 Zero-Shot CLIP models on CFD App. A.8
A.1 TECHNIQUAL DETAILS
Representation Disentanglement: To ensure that the mapping functions avoid learning representa- tions with redundant information where different dimensions are highly correlated with each other, seek compact (Bengio et al., (2013) debiased embedding space. Therefore, we impose additional constraints on the representation. Specifically, we constrain the search space of the mapping functions f()) to learn disentangled representation (Bengio et al. 2013) follows
A, = (Uf.... f+) fs Hxx Cov (x + (fi, fihux =
In the above set, Cov (X)(X), (X)) part enforces the covariance matrix of f(X) to be an identity matrix and encourages the variance of each entry of Z to be one and different entries of Z to be uncorrelated with each other. The regularization part, 7 (fi, f2) Rix encourages the components of the mapping functions to be of unit norm and as orthogonal as possible to each other. These constraints also aid with numerical stability during empirical estimation (Fukumizu et al. 2007).
Details of training FairerCLIP are presented in Algorithm | FairerCLIP uses representation of images, representation of texts corresponding target attribute labels (Xx), and representation of text corresponding to sensitive attribute labels (Xrs) as its inputs. FairerCLIPS goal find the image encoder (ff) and text encoder (ft) that can map the biased features generated by the CLIP to debiased representation space. The training algorithm starts with initializing the label predictions. Since this algorithm is used for scenarios where we do not have access to the ground-truth labels of target attributes and sensitive attributes, need to predict them by zero-shot classification from CLIP features. However, scenarios where we have access to the true labels of skip the pseudo Y initialization step and use the ground truth Y instead. In
Training Algorithm:
the target attribute, the last step of initialization, we need to initialize the representation of images since we are using an alternating method to optimize the parameters of both the image encoder and text encoder. After the initialization step, we start to train both models alternatingly. After each optimization iteration, we update our prediction of Y labels. After reaching the stop condition, the training process is complete.
A.2 PROOFS
Lemma 1. Let Kx,, Kxr RRan be the Gram matrices corresponding to Hx, and Hx,, re- spectively, i.e., (Kx,); )ij = kx, (11)) and (K (Kxr)s = ks(Tr,, &Ty), where covariance empirically estimated
Cov (F:(X1) ~ Σ
Σ
Σ
Im (wTT. ).
n² p=
k=1
It follows that, the corresponding empirical estimator for Dep (Z,, Zr) is
Dep (Z,, Zr) = n² |0,Kx, HKx, θ
(10)
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Algorithm 1: FairerCLIP Training Without Labels
Input:
X₁ E = Xr E Rl Xrs E R 8ixd m E N
Output:
Initialize:
Y Vaz E X1,0T E XT argmax
2i By ler
//* initialize pseudo Y
#r
-
YaI E XI, E Xrs argmax
Bey f
} //x initialize pseudo
/
eTS
0 (Ji(X)) argmax
TI ,%) (000)) Eq. ?
f1
i 0;
while < m do
2 - [fi(X+)]ff
)}: J(rr Xr, 1 "0,S,22 ( }}: /a solve Eq.
argmax
fT
2 3/40 + (fi(Xi))f)
argmax
J TI, X1, 8()) S, 2 }3 /x solve Eq.
f1
{(+1) - -(vzr 21(+1)220 zr E = [1+1]
argmax
x sт | =r
// refine pseudo Y
i i+
end
Proof.
2
n Σ p=1
Dep(Zr, ZT)
f (m.
Sg (Tt,))
Jm (aTT))
71
n12
j=l
Σ
0 Kx
Xx 0rm
07, Kx 1 1, K Xx Qrm
n
1, K HK HK 0mm
1O,Kx K HK x, 0
n 0
0
(1I)
Theorem 2. Let the Cholesky factorization of Kx be Kx Lx Lx where Lx Ruxd (d S n) a full column-rank matrix. Let r S d, then a solution to
1 |e HI
(0K>HLs 1 OK H 1
max (J 2o) = n YEA.
(12)
is
(#1, f (X) rpl (xx( X). Ax (2,, X))²
where 0MM- LT and the columns of U are eigenvectors corresponding to the largest eigenvalues of the following generalized eigenvalue problem.
I (HKyH- H + H.. Lxu Lxu=X (=IRHLX + x11 u.
(13)
Furthermore, the supremum value of the objective function equal to E/=1 Aj. where (AI, Ar
are r largest eigenvalues of equation 13
Proof. Using the representer theorem, the disentanglement property in Ar ((i,... ,5) St, Hx. Cov (x(( + (Ji, JiHNs dig].
(14)
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can be expressed
Cov (f((X), (x)) + (frs fihux
(Z)))
1
Σ f((ak) Σ
fy(m)) +7 ffi
n²
m=1
K (x
K x ( Fm00pn
0 K x 1x + (fi. fihM
n²
k=
m=1
(Kx (Kx0,) 112 x +7
Σ 0x 2k), Σ (.
Hx
I O0 K[[KOxOO1 +7 Ox
"
1 oft Lx (L HLx + m 1) 03
= 8igj-
As result, f Ar equivalent to
( L Hbx + 71) L = I,
O0x
where ④ [01.... .0, 00,""
grxx
Let V _ TT and consider the optimization problem in equation 7
|0Kx I|OK + |0xxH20
sup fEA. n²
HK, - +,
= sup
fEA. n²
1 Tr (T((OxxB
= max vICV=I, n
max VFCV=I, n
[ BV]
(15)
where
B = -
Lx
shown Kokiopoulou et al. 2011 that ard optimiieer 15 is any matrix U whose columns eigenvectors corresponding largest eigenvalues of generalized problem
Bu = Cu
(16)
and the maximum value is the summation of r largest eigenvalues. Once U is determined, then, any θ in which L T U is optimal O (denoted by OOP). Note that Opt is not unique and has general form of
-"= - Au. R(Ao) S (L*).
However, setting Ao to zero would lead to minimum norm for O. Therefore, we opt Opp UTL X-
A.3 IMPLEMENTATION DETAILS
We conducted experiments on CelebA, Waterbirds, FairFace, and the Chicago Face Dataset (CFD). For CelebA and Waterbirds, follow their official train/val/test splits and only use ground truth
Optimal V is not unique.
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labels from the val split for hyperparameter tuning. For CFD, since there is no official dataset split. we randomly split it with a ratio of 0.5/0. 1 for train/val/test. Following the standard setting Zhang & Ré (0022) we use val split to decide the optimal T, T2. and dimensionality of the random Fourier features (RFF). For CelebA, the optimal т. T:, and RFF dimensions are 0.8, 0.5, and 8000. For Waterbirds, the optimal 7, T2. and RFF dimensions are 0.7, 0.7, and 3000. And for CFD, the optimal T2. and RFF dimensions are 0.6, 0.3, and 1000. In the scenario where the group labels are not available, we follow the same setup as the scenario where the group labels of the val split are available. For the CelebA dataset, we also conduct pre- sampling process on the training split to balance the number of each class from the predicted ÿ.
For the FairFace dataset, we use 10 text prompts that are unrelated to the facial attributes or the sensitive attributes of the samples. In this setting, the sensitive attribute is gender or race, and the text prompts are constructed as "This is a photo of a [attribute] person" where /attribute/ I can be one of the elements of the (good, evil, smart, dumb, attractive, unattractive, lawful, criminal, friendly, unfriendly| set.
For all the above-mentioned experiments under different settings, we set the representation dimen- sionality r to where e is the number of classes of the downstream target task.
For clarity, we summarized all the above-mentioned implementation details Tab.
Table 6: Implementation details of FairerCLIPP for each dataset.
Dataset
RFF Dim.
Tz
Train/Val/Te
Training samples
CelebA
8000
0.8
0.5
Official Splits
162,770
Waterbirds
3000
0.7
0.7
Official Splits
4,795
FairFace
3000
0.8
0.8
Official Splits
86,744
CFD
1000
0.6
0.3
0.5/0.1/0.4
298
A.4 NUMERICAL COMPLEXITY
Computational Complexity: If Lx in equation 13 is provided in the training dataset, then the computational complexity of obtaining the optimal encoder is ( where / < n is the numerical rank of the Gram matrix Kx. However, the dominating part of the computational complexity is due to the Cholesky factorization, Kx Lx which is O(n²). Using random Fourier features (RFF) (Rahimi & Recht 2007], lix (æ, æ') can be approximated by rx (x=)" rx(x'), where Tx (x) E Rd. In this situation, the Cholesky factorization can be directly calculated as
rx (z1))
Lx
:
Rand
(17)
(x))
As result, the computational complexity of obtaining the optimal encoder becomes (d)), where the RFF dimension, d, can be significantly less than the sample size n with negligible error the approximation kx (ar, x²) zs rx (x:) rx (a:).
Memory Complexity: The memory complexity of equation 13 if calculated naively, is (nn)) since Ky. Ks, and Zo 2 are n by matrices. However, using RFF together with Cholesky factorization Ky = Ly Ly, Ks = L the left-hand side of equation 13 can be ce-arranged as
L Lx [LfLx)
LI
[L5Lx)
+
L 2o)
2 Lx).
(18)
where To = HZo = Zo " (1, and Ż = HLy = Ly 11, (1. therefore, the required memory complexity is O(nd). Note that L and HLx can be calculated similarly.
A.5 EFFECT OF DATA SIZE ON THE PERFORMANCE OF FAIRERCLIP
To evaluate the effectiveness of the FairerCLIP under limited data samples condition we report the performance of FairerCLIP the size of the training dataset is varied. In this experiment,
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Avg Accuracy
WG Accuracy
155
15.0
87.
870
GC
01
03
05
66
CO 65
08
Figure 6: Effect of r and т: on the average accuracy (left) and worst group accuracy (right). For every fixed T2, the worst group accuracy increases with 7 and then starts to decrease beyond a point. Additionally, the variation of the metrics is smooth over the range of hyperparameters, indicating lower sensitivity towards them.
randomly sampled 5, 25, 50, 75, and 100 percent of the training data as our training set. Then the Avg.. WG, and Gap are evaluated. Tsooss the results of the evaluation for the Waterbird and CelebA datasets. The results indicate that FairerCLIP is able to perform sufficiently well when small sub-sample of the dataset used for training. More specifically, FairerCI.IP is only losing 2.9% of its WG accuracy when only 25% of the original training data is employed in its training phase. Morcover, on Waterbirds, relatively smaller dataset, it loses less than 6% of WG accuracy when only 50% of training data is used. This experiment shows the effectiveness of FairerCLIP under a limited number of training samples.
the CelebA dataset,
Table 7: Effect of training data size on the performance of FairerCLIP
CelebA
Waterbird
Samples
Avg. (f)
WG (f)
Gap (1)
Avg. (t)
WG (t)
Gap (1)
5%
84.21
73.88
10.32
86.31
65.26
21.05
25%
88.26
83.88
4.37
86.54
78.66
7.88
50%
86.65
84.29
2.36
89.11
81.31
7.80
75%
90.44
85.56
4.88
87.04
84.11
2.92
100%
89.3
86.7
2.6
92.30
87.70
4.60
A.6 MORE ABLATION STUDIES
In studied the effect of different components of FairerCLIP such as Dep(Z, Y). Dep (1, )r and updating the prediction the target labels each iteration. Here, we add another ablation study the effect of our control hyper-parameters, and тN on the performance of the method.
Effect of and 7=: We illustrate the performance of average accuracy and worst group's accuracy for varying values of and т. in Figure First, and т. vary, there is smooth change in the average and worst group accuracy, which demonstrates the stability of FairerCLIP. Second, as 7 increases, the worst group's accuracy also improves, which alludes the effectiveness of as control for the degree of debiasing. However, when reaches certain value, further increase in its value leads to degradation in the worst group's accuracy. Similarly, т, also plays a gradual but noticeable effect on improving both the average and worst group accuracy for given value of т.
A.7 COMPARING FEATURES OF CLIP VIT-L/14 AND CLIP RESNET-50
To compare the features of CLIP ViT-L/14 and CLIP ResNet-50, measure the amount of infor- mation from the target attribute and sensitive attribute contained in their generated representations. The embedded information measured in terms of statistical dependency between the features and
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Published as a conference paper at ICLR 2024
Table 8: Comparison between CLIP ViT-L/14 and CLIP ResNet-50 in terms of the amount of the information from Y and S that each of them can embed into their feature space on the Waterbirds.
(X, Y)
(X,, S)
HSIC
KCC
HSIC
KCC
CLIP ViT-L/14
0.1849
0.8267
0.2392
0.8661
CLIP ResNet-50
0.1423
0.7556
0.3823
0.8861
their ground-truth and S labels. To calculate these dependencies, Selbbrt--Schiidt independence criterion (HSIC) (Gretton et al., (005)) and Kernel Canonical Covariance (KCC) Bach & Jordan (2002) are used. Tab. | compares these two CLIP models. From the table, we can observe that CLIP ViT-L/14 embeds more information about the target Y while containing less information about S which indicates that the former provides better features for the Waterbirds dataset.
A.8 COMPARING MORE THANN 100 ZERO-SHOT CLIP MODELS ON CFD
4 4.2.2 zero-shot classification CFD is difficult task for the OpenAI CLIP model. In Figure 7 we show that a majority of other publicly available CLIP models suffer similarly CFD. In fact, several models achieve only near-zero WG accuracy, irrespective of their parameter count and the training dataset.
As mentioned in Section
80
Dataset
COCO
WebLi
CC12M
70
DataComp
YFCC
LAION2B
Num Params
Avg (%)
Merged2B
500
60
MetaCLIP
1000
CommonPool
1500
LAION400M
2000
OpenAl WIT
2500
40
10
20
50
60
WG (%)
Figure 7: Comparison of more than 100 publicly available CLIP models zero-shot performance CFD dataset. Colors show the pre-trained dataset and sizes show the number of parameters each model.
18


Reviews:
[{"review_id": "EgV7yDhuMe", "reviewer": "Reviewer_p1sH", "summary": "This paper proposes FairVLM, which is an additional module top on the frozen CLIP features, to de-bias the prediction. Using the frozen CLIP vision and text encoders, the proposed method extracts visual and texture features, and lets them be de-biased using the Hilbert-Schmidt Independence Criterion (HSIC), a famous approach in de-bias literature. Instead of using the original HSIC, this paper proposes to use a simplified version following Sadeghi et al. The proposed simplified HSIC provides a closed-form solution to the additional feature encoders when the features are fixed. To make the method efficient, this paper proposes to approximate Cholesky decomposition using random Fourier features (RFF), resulting in reducing the computational complexity from $O(n^3)$ to $O(n^2)$. Experimental results show that the proposed method shows the effectiveness of the proposed method in both intrinsic dependency (i.e., fairness scenario) and spurious correlation.", "questions": "I think the technical contribution of this paper is sound and empirical evaluation results look reasonable. However, this paper has a critical problem in its writing and presentation, including many missing related works and details. Please check my initial review and respond to my concerns. Specifically, I would like to clarify all the details of how the method is implemented and trained in the revised version, which is not presented in the initial version. I think the initial version is improper to be published as an ICLR paper, but my concern is mostly around the writing, that could be improved during the revision period. I presume that the revised manuscript will need significant efforts, but mainly in the presentation, rather than technical enhancement. Hence, if the revised manuscript is sound and can resolve my initial concerns, I am willing to update my score.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "HSIC is a promising approach to achieving de-biased representations, as many previous studies have observed. This paper successfully brings the advantage of HSIC to CLIP feature refinement tasks. I also think that this paper has a non-trivial contribution to introducing the closed-form solutions used for updating the parameters, including the approximated version of Cholesky decomposition using RFF. Straightforwardly, an iterative algorithm using a closed-form solution will converge much faster than gradient-based algorithms, as shown in classic machine learning studies, such as ADMM [R1]\n\n- [R1] Boyd, Stephen, et al. \"Distributed optimization and statistical learning via the alternating direction method of multipliers.\" Foundations and Trends® in Machine learning 3.1 (2011): 1-122.\n\nCombining two good properties (HSIC, a promising approach, and an efficient update algorithm using a closed-form solution), the proposed method shows promising performances on the given evaluation benchmarks.", "weaknesses": "### Scope of the paper\n\nThe terminology \"VLM\" is misused in this paper. VLM literally includes a vast area of models trained with vision and language. For example, visual-question answering (VQA) is a VLM model, vision-language pre-training (VLP) with cross-attention transformers (such as ViLBERT [R2], ViLT [R3], Align [R4], VinVL [R5], ALBEF [R6], BLIP [R7]) is VLM, multi-modal generation models, such as dall-e 1, 2 and 3, stable diffusion or dreambooth, are VLM, and recent language-model combined vision models, such as BLIP2 [R8], Fromage [R9], GPT-4, are VLM. (I omitted some famous works, such as dall-e, SD, GPT ...).\n\n- [R2] Lu, Jiasen, et al. \"Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.\" Advances in neural information processing systems 32 (2019).\n- [R3] Kim, Wonjae, Bokyung Son, and Ildoo Kim. \"Vilt: Vision-and-language transformer without convolution or region supervision.\" International Conference on Machine Learning. PMLR, 2021.\n- [R4] Jia, Chao, et al. \"Scaling up visual and vision-language representation learning with noisy text supervision.\" International conference on machine learning. PMLR, 2021.\n- [R5] Zhang, Pengchuan, et al. \"Vinvl: Revisiting visual representations in vision-language models.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.\n- [R6] Li, Junnan, et al. \"Align before fuse: Vision and language representation learning with momentum distillation.\" Advances in neural information processing systems 34 (2021): 9694-9705.\n- [R7] Li, Junnan, et al. \"Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.\" International Conference on Machine Learning. PMLR, 2022.\n- [R8] Li, Junnan, et al. \"Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.\" arXiv preprint arXiv:2301.12597 (2023).\n- [R9] Koh, Jing Yu, Ruslan Salakhutdinov, and Daniel Fried. \"Grounding language models to images for multimodal generation.\" arXiv preprint arXiv:2301.13823 (2023).\n\nHowever, the scope of this paper is very narrow compared to the entire VLM family. I feel that the title \"FairVLM\" and the terminology \"VLM\" are too much overclaimed, and a reader can misunderstand the focus of this paper. I think this paper should tone down its contribution and focus more specifically. This paper only targets a feature refinement method top on the frozen CLIP model, and the comparison methods are also methods using frozen CLIP encoders. Note that it is still a narrow topic in \"de-biasing CLIP zero-shot prediction\" because a number of works focus on the fine-tuning strategy [R10-12]. On the other hand, this paper relies on the pre-trained feature encoders that may weakens its contribution.\n\n- [R10] Wortsman, Mitchell, et al. \"Robust fine-tuning of zero-shot models.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.\n- [R11] So, Junhyuk, et al. \"Geodesic multi-modal mixup for robust fine-tuning.\" arXiv preprint arXiv:2203.03897 (2022).\n- [R12] Vogt-Lowell, Kevin, et al. \"Robust Fine-Tuning of Vision-Language Models for Domain Generalization.\" IEEE High Performance Extreme Computing Conference (HPEC). 2023.\n\n### Missing related works\n\nIncluding [R1-12] in the first comment, there are a number of missing related works that should be discussed. For example, HSIC is a popular approach in de-biasing studies [R13, R14]. R13 directly optimizes the HSIC between features and sensitive attribute labels, similar to Dep(Z, Y) in the paper; R14 optimizes the HSIC between biased features and target features to avoid using sensitive attribute labels Y. If we extend our viewpoint to RHKS, there is work using MMD to achieve fairness [R15]. I omitted many HSIC-based regularization methods that could be related to this work, but if possible, it would be great to add more citations for methods using HSIC.\n\n- [R13] Quadrianto, Novi, Viktoriia Sharmanska, and Oliver Thomas. \"Discovering fair representations in the data domain.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.\n- [R14] Bahng, Hyojin, et al. \"Learning de-biased representations with biased representations.\" International Conference on Machine Learning. PMLR, 2020.\n- [R15] Jung, Sangwon, et al. \"Fair feature distillation for visual recognition.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.\n\nAlso, in terms of employing pseudo-labels for the de-biasing optimization, I think this paper is also related to [R16]; where R16 is based on semi-supervised learning without the pseudo-label refinement process.\n\n- [R16] Jung, Sangwon, Sanghyuk Chun, and Taesup Moon. \"Learning fair classifiers with partially annotated group labels.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.\n\n### Unclear or missing details\n\nWhile reading the paper, I had trouble understanding the details of the paper. For example, it is still unclear to me how the \"encoder\" works. I presume that the encoder parameter $\\Theta$ is a linear projection and an RBF kernel is used for computing HSIC, but it is unclear. Also, there is no description of $r$ (r becomes the dimensionality of the generated representation) and the value of $r$ as well. It means that this paper does not provide any detail of RKHS hyperparameters, such as the dimensionality of the projection layer and the hyperparameter of the kernel method. Similarly, I cannot find any detail of the choice of the learning hyperparameters, such as the number of iterations, and batch size. It means that it is impossible or extremely difficult to reproduce the results in this paper. Overall, this paper is very hard to understand the method details and implementation details, although I think this paper has certain contributions in terms of the methodology development.", "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": "sFmhTst7Fn", "reviewer": "Reviewer_2eeh", "summary": "This paper introduces FairVLM, a novel approach designed to address bias in zero-shot predictions made by VLMs. FairVLM demonstrates versatility in mitigating bias arising from two primary sources: spurious correlations and intrinsic dependencies within the data. Moreover, it offers the flexibility to be trained with or without the presence of ground-truth labels.", "questions": "I don't have any questions because I cannot understant the method part.", "limitations": "", "rating": 8, "confidence": 1, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. The paper demonstrates that a single general method can debias the image and text features of VLMs under different scenarios more effectively than specialized solutions for each scenario. The scenarios include accounting for both spurious correlations and intrinsic dependencies, learning with and without ground-truth labels, and learning from small and medium-sized datasets\n2. The words are fluent.", "weaknesses": "1. The experiment results on the datasets (w/ labels) are not good enough.\n2. The pare of the method is too complex.", "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": "rM8hiPzBaQ", "reviewer": "Reviewer_9VkB", "summary": "This paper suggested a VLM debiasing method that remove bias in visual and text representations jointly by using reproducing kernel Hilbert space and deploying statistical dependency measure in RKHS. This enables to considering nonlinearity between the representation and the attribute. This paper provides a closed form solution for such formulation, and theoretical analysis on its complexity. Experiments show that the suggested method can work well both with and without true labels setting.", "questions": "- Can this method be extended to a single point debiasing? (i.e. a single point inference for online prediction?)\n- It looks like FairVLM sacrifices average scores more than other methods such as Contrastive Adapter in Table 2 w/ labels result. Furthermore, FairVLM works similarly in w/labels and w/o labels in CelebA. What’s a good interpretation on this?\n- Table 3 CFD results look interesting! Does it imply FairVLM has its strength when the number of training samples is limited? How do other zero-shot methods in CFD dataset?\n- Is there any convergence guarantee for Algorithm 1 (FairVLM Training Without Labels)? Also, I am wondering if there can be a failure mode that the errors in the initialization step propagate further in iteration steps.\n- Possibly related works\n    - Chen, A. S., Lee, Y., Setlur, A., Levine, S., & Finn, C. (2023). Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features. *arXiv preprint arXiv:2302.05441*.\n    - Adila, D., Shin, C., Cai, L., & Sala, F. (2023). Zero-Shot Robustification of Zero-Shot Models With Foundation Models. *arXiv preprint arXiv:2309.04344*.\n    - An, B., Zhu, S., Panaitescu-Liess, M. A., Mummadi, C. K., & Huang, F. (2023, July). More Context, Less Distraction: Improving Zero-Shot Inference of CLIP by Inferring and Describing Spurious Features. In *Workshop on Efficient Systems for Foundation Models@ ICML2023*.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Paper is well written and organization is clear.\n- While previous methods mainly depend on the linearity of representation and the attribute, the suggested method can overcome such linearity assumption using RKHS.\n- The suggested method is practically competitive in both of settings — w or w/o labels.\n- Ablation experiments cover various scenarios, providing solid understanding of variables that affect performance.", "weaknesses": "While one of core features of VLM is zero-shot classification, the suggested method still requires parameter tuning (RBF kernel parameter) and stacking test data, which could be a limitation of the method.", "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": "CsrrGrPdSc", "reviewer": "Reviewer_75gn", "summary": "In this work, the authors address the fairness problems in visual-language models (VLMs). More specifically, they propose a framework for jointly debiasing VLMs’ image and text representations. The proposed framework utilizes an alternating optimization-based approach to debias VLM representations. The authors evaluate their work using several datasets and show that FairVLM alleviates the debiasing problems of vanilla VLMs.", "questions": "Please refer to the weakness section for more details.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Compared to previous debiasing works in VLMs, FairVLM results in the debiasing of both image and textual representations.\n\n2. FairVLM is agnostic to the availability of data labels and can generate debiased representations with or without labels.\n\n3. Using the properties of RKHS in mapping the original VLM representations to a debiased space is interesting.", "weaknesses": "1. The authors did not compare their results with unimodal baselines, i.e., techniques that debias only the image/text representations of the VLM.\n\n2. While the authors argue that RKHS has nice universal approximation properties, it's unclear how and why they aid in debiasing the original representations.", "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)