Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes
Authors: Fengda Zhang
Kun Kuang
Long Chen
Yuxuan Liu
Chao Wu
Jun Xiao
Venue: ICLR 2023 poster (2023)
Keywords: Fair Representation Learning
Semi-supervised Learning
Contrastive Learning
Data Augmentation

Abstract: Learning high-quality representation is important and essential for visual recognition. Unfortunately, traditional representation learning suffers from fairness issues since the model may learn information of sensitive attributes. Recently, a series of studies have been proposed to improve fairness by explicitly decorrelating target labels and sensitive attributes. Most of these methods, however, rely on the assumption that fully annotated labels on target variable and sensitive attributes are available, which is unrealistic due to the expensive annotation cost. In this paper, we investigate a novel and practical problem of Fair Unsupervised Representation Learning with Partially annotated Sensitive labels (FURL-PS). FURL-PS has two key challenges: 1) how to make full use of the samples that are not annotated with sensitive attributes; 2) how to eliminate bias in the dataset without target labels. To address these challenges, we propose a general Fairness-aware Contrastive Learning (FairCL) framework consisting of two stages. Firstly, we generate contrastive sample pairs, which share the same visual information apart from sensitive attributes, for each instance in the original dataset. In this way, we construct a balanced and unbiased dataset. Then, we execute fair contrastive learning by closing the distance between representations of contrastive sample pairs. Besides, we also propose an unsupervised way to balance the utility and fairness of learned representations by feature reweighting. Extensive experimental results illustrate the effectiveness of our method in terms of fairness and utility, even with very limited sensitive attributes and serious data bias.

Published as a conference paper at ICLR 2023
PAIRNESS-AWARE CONTRASTIVE LEARNING WITH PARTIALLY ANNOTATED SNSSITIVE ATTRIBUTES
Fengda Zhang', Kun 2 Long Chen², Yuxuan Liu', Chao Wu', Jun Xiao "Zeejiang University, "Key Laboratory for Corneal Diseases Research of Zhejiang Province The Hong Kong University of Science and Technology {fdzhang, kunkuang, yuxuanliu, chao. wu e ju. edu. en, z @juchenlong@gmaii com, junx zju. edu. on
ABSTRACT
Learning high-quality representation is important and essential for visual recogni- tion. Unfortunately, traditional representation learning suffers from fairness issues since the model may learn information of sensitive attributes. Recently, a series studies have been proposed to improve fairness by explicitly decorrelating target labels and sensitive attributes. Most of these methods, however, rely on the as- sumption that fully annotated labels on target variable and sensitive attributes are available, which is unrealistic due to the expensive annotation cost. In this paper, we investigate a novel and practical problem of Fair Unsupervised Representation Learning with Partially annotated Sensitive labels (FURL-PS)). FURL-PS has two key challenges: 1) how to make full use of the samples that are not annotated with sensitive attributes; 2) how to eliminate bias in the dataset without target labels. To address these challenges, we propose a general Fairness-aware Contrastive Learning (FairCL) framework consisting of two stages. Firstly, generate con- trastive sample pairs, which share the same visual information apart from sensitive attributes, for each instance in the original dataset. In this way, construct bal- anced and unbiased dataset. Then, we execute fair contrastive learning by closing the distance between representations of contrastive sample pairs. Besides, we also propose an unsupervised way to balance the utility and fairness of learned rep- resentations by feature reweighting. Extensive experimental results illustrate the effectiveness of our method in terms of fairness and utility, even with very limited sensitive attributes and serious data bias.
INTRODUCTION
Learning powerful representation takes an important role in visual recognition, and there are lot of works proposed to learn visual representations (Bengio et al., 2013; Kolesnikov et al., 2019; Wang et al., 2020a; Liu et al., 2022). Among them, contrastive learning achieves state-of-the-art performance on various vision tasks (Tian et al., 2020; Chuang et al., 2020). Contrastive learning first generates views from original images by random data augmentation, and the views from the same image are defined as positive samples. Then the model can learn effective representations by closing the distance between representations of positive samples, while being protected from mode collapse via an additional module such as negative samples (Chen et al., 2020a; He et al., 2020; Chen al., 2020b), momentum update (Grill et al., 2020), and stopping gradient (Chen He, 2021).
Unfortunately, traditional representation learning methods ignore potential fairness issues, which becomes increasing concern as recognition systems are widely used in the real world (Zemel et al., 2013; Madras et al., 2018; Creager et al., 2019; Ly et al., 2023). For example. the model trained by contrastive learning may learn the information of sensitive attributes (e.g., gender, race) by using it as a shortcut to minimize the distance between representations of positive samples in the training stage, since the positive samples have the same sensitive attributes. As a result, decisions based on biased representation models may discriminate against certain groups or individuals in practice, by using spurious correlations between predictive target and sensitive attributes (Wang
"Corresponding author.

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Published as a conference paper at ICLR 2023
et al., 2020b; Park et al., 2021; Zhang al., 2021). Therefore, how to develop fair representation model is of paramount importance for both academic research and real applications.
Most of existing works achieve faireess via decorrelating target labels and sensitive attributes plicitly, which rely on the data annotations (Mchrabi ct al., 2021; Wu et al., 2022; Zhang et al., 2022; Zhu et al., 2022). However, assuming that all data have fully annotated labels can be unrealistic (Liu et al., 2016; Zhang et al., 2020b; Shao et al., 2021; Jung et al., 2022; Song et al., 2023). In many real scenarios, the target labels and even downstream tasks are not provided, and all we have are images and limited annotations of sensitive attributes. Data labels require additional expensive cost of human annotations, which naturally leads us to ask the following question: Can we train a fair unsupervised representation model with only partially annotated sensitive attributes?
In this paper, we investigate a practical and novel problem of Fair Unsupervised Representation Learning with Partially annotated Sensitive attributes (FURL-PS). Our goal is to utilize the images and limited sensitive labels to learn visual representations that can be used for various downstream tasks of visual recognition, while achieving fairness by being minimally correlated with sensitive at- tributes. It is challenging to solve the proposed problem. Firstly, most samples are not labeled with sensitive attributes. A natural idea is to pseudo-label the unlabeled data by sensitive attribute clas- sifier. However, it is not advisable to train a representation model on the data with pseudo-sensitive labels, since the noises in pscudo labels may severely affect the faimess performance. Secondly, there may be data imbalance between demographic groups. Assuming that the female group has a large proportion of samples of blond hair, while the male group has the opposite proportion. As result, the models trained on the above biased data may learn spurious correlation between gender and blond hair. Unfortunately, it is difficult to balance the data distribution of different groups with- out the prior of downstream tasks or annotated target labels. Generally, FURL-PS problem has two main challenges: 1) How make full use of the data that are not annotated with sensitive attributes? 2) How to balance the possible agnostic bias in data without target labels?
To address these challenges, our idea is to construct a balanced dataset annotated with sensitive labels based on the original dataset, and then train representation model with fair contrastive learn- ing on the unbiased dataset. We propose a two-stage Fairness-aware Contrastive Learning (FairCL) framework to implement the above idea. In the first stage, we design a semi-supervised learning algorithm to train the image attribute editor with limited sensitive labels, which is used to edit the pre-defined sensitive attributes of a given image. In the second stage, train a representation model by fair contrastive learning with balanced augmentation. Specifically, based on the image attribute editor, we can generate contrastive sample pairs, which share the same visual information apart from sensitive attributes (e.g., male and female), for each sample in the original dataset. By closing the distance between representations of contrastive sample pairs, the model can learn powerful and fair representations. Our approach has two advantages: 1) we can get the utmost out of unlabeled im- ages by generating samples with given sensitive attributes from them; 2) the augmented dataset is unbiased, since it consists of contrastive sample pairs and thus the data proportions are naturally balanced for different demographic groups. Furthermore, we also develop an unsupervised way to balance the utility and fairness of learned representations by feature reweighting.
We validate the effectiveness of our method on two facial attribute recognition datasets: CelebA (Liu et al., 2018) and UTTK-Face (Zhang et al., 2017). Extensive experimental results show that the pro- posed method outperforms the existing unsupervised learning methods in terms of both classifica- tion accuracy and fairness, and even achieves comparable performance with the semi-supervised methods that require annotations on the target labels. Besides, our method is robust to the ratio of sensitive labels and severity of data bias. Furthermore, we also show the extensibility of our general framework to different contrastive learning algorithms through experiments.
Main Contributions: 1) To the best our knowledge, we are the first one to propose the practical and challenging problem of Fair Unsupervised Representation Learning with only Partially annotated Sensitive attributes (FURL-PS). 2) We develop the Fairness-aware Contrastive Learning (FairCL. framework to solve the proposed problem, which can be compatible with all of contrastive learning algorithms to learn a fair and powerful representation model. 3) Extensive experiments illustrate the effectiveness of our proposed method in terms of fairness and utility.
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2 RELATED WORK
Fairness in Unsupervised and Semi-supervised Learning. There are three branches to guarantee fairness in unsupervised learning. Firstly, fair feature selection, a pre-processing paradigm, finds a subset of features that preserve the original information as much as possible while being mini- mally correlated with sensitive attributes (Grgic-Hluee Haaic- al., 2016; Grgié- et al., 2018; Xing et al., 2021). However, this kind of methods is designed for structured data, and cannot be applied to images data and deep models. Secondly, fair clustering balances the distribution of different subgroups formed by sensitive attributes in each cluster, but it cannot yield a model for various downstream tasks (Chierichetti et al., 2017; Kleindessner et al., 2019; Li et al., 2020). At last, some studies based fair representation learning have been bringing paradigm for fair unsupervised learning (Louizos et al., 2015; Raff & Sylvester, 2018). As for fair semi-supervised learning, most existing methods first pseudo-label the unlabeled data via classifier, and then train model these data with fairness constraints (Jung et al., 2022; Zhang et al., 2020b;c). However, the pseudo-label noise may exacerbate model unfairness in turn. Instead, our proposed method does not directly use pseudo-labels when training the fair representation model.
Contrastive Learning. Self-supervised contrastive learning provides a representation learning paradigm without target labels, and achieves better accuracy than the state-of-the-art methods on various tasks (Xiao al., 2020; Zhang et al., 2020a). Some methods such as SimCLR (Chen al., 2020a) and MoCo (He et al., 2020) first define the positive/negative samples as patches generated from the dame/different images via random data augmentation, and then train a representation model by closing/pushing away the distance between representations of positive/negative samples. Re- cent studies argue that negative samples are not necessary for contrastive learning, and they use some techniques, e-g., momentum update (Grill et al., 2020) and stopping gradient (Chen & He, 2021) to protect the model from mode collapse instead of negative samples. Afterwards, supervised contrastive learning outperforms other otate-of-the-at methods based on traditional cross-entropy loss (Khosla et al., 2020). However, existing self-supervised contrastive learning methods ignore potential faimess issues. To this end, FSCL proposes a fair supervised contrastive loss train fair representation model (Park et al., 2022). However, FSCL relies on target labels and sensitive attributes. Besides, FSCL is based on supervised contrastive learning which needs negative sam- ples, while our proposed framework is general to be applied to any contrastive learning algorithm to improve fairness.
Image Generation. Our proposed method involves the task of image attribute editing, which takes an image as input and aims to generate a new image with desired attributes while preserving other details (Liu et al., 2019; He et al., 2019; Dogan & Keles, 2020). We emphasize that advances in the field of image attribute editing can help improve the performance of our work, since the subsequent methods can also be used here. Some studies aim to construct balanced and unbiased dataset by data augmentation (Ramaswamy et al., 2021). However, they need the prior of downstream task to generate new samples. Recent works have proposed to evaluate counterfactual fairness by generating counterfactual samples (Denton et al., 2019; Joo & Kärkkäänen, 2020; Dash et al., 2022). Different from them, consider more challenging problem to train fair representation model. Morcover, we consider a more practical setting where there are no target labels and fully annotated sensitive attributes.
METHOD
In this section, we start with brief introduction of the problem formulation of FURL-PS and overall flow of our proposed method in Sec. 3.1. Then we display how to generate augmented samples of
different sensitive attributes with limited annotated sensitive labels in Sec. 3.2. We elaborate how to execute fair contrastive learning with balanced augmentation in Sec. 3.3. Lastly, to balance the
trade-off between utility and fairness of learned representations, we propose feature reweighting module for those sensitive atriibute-dependent sub-features in Sec. 3.4.
3.1 PROBLEM FORMULATION AND OVERALL FLOW
Assume that we have n original images {ax k= 1...... where D4 E X C Rd. Labeled dataset is ai 1 where ny is the number of images with annotated sensitive labels, and
denoted as Dj (IA, k 8k
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wal or Tale?
drop
110
2-00
x
Leta
Unlabeled Data D,
shure
Gdsc
Gdec
G
yes
$o
cf thr
inforence training
51
x=
Gene
no
drop
x (s
(a) Architecture of Image ttribute Editor G
(b) Training Classifier C and Generator G by Mutual Promotion
Figure I: Semi-supervised Learning of Image Sensitive Attribute Editor.
Sg E 10.1,.., Ms 1} represents the sensitive attribute label (e.g., male and female). Unlabeled dataset denoted as D.. {xx ) (2 The target labels {= training state, where y/ {0, 1..., My 1}. In this paper, we assume that both target labels and sensitive attributes are binary variables for convenience, i.e., Ms = My = 2. We emphasize, however, that proposed problem and framework can be easily generalized to multivariate setting. The goal is to train an effective and fair encoder network F(.) that maps the image Zy. E X into representation h& H. where the representations not discriminating against demographic groups with given sensitive attributes.
not available in the
be used for various downstream tasks while
Our proposed method consists of two stages: 1) Contrastive Sample Generation and 2) Fairness- aware Contrastive Learning. We first define the contrastive samples as a pair of images that share the same visual information except for sensitive attributes. In the first stage, our goal is prepare contrastive samples {(*f, = k==122..,n based on the original dataset. To achieve it, we design semi-supervised algorithm to train an image sensitive attribute editor G(.-). which takes an image Jy and sensitive attribute E {0, }} as input and can map the original image to a new image zt with given sensitive attribute s while keeping other information as unchanged as possible. In the second stage, we execute fairness-awan aware contrastive learning on the augmented dataset to train representation model F(-) without any target label or sensitive label.
3.2 CONTRASTIVE SAMPLE GENERATION WITH LIMITED SENSITIVE ATTRIBUTE LABELS
We start with training a generative model used to generate contrastive samples. We implement based on AtGAN (He et al., 2019), but emphasize that any approach designed for image attribute editing can be adapted to be a backbone method. The training architecture of generative model G is shown in Figure (aa). The encoder Gene maps input image to latent representation Then the decoder Gdee takes z and sensitive attributes as input, and generates images with corresponding sensitive attributes. There are three loss optimized jointly: 1) discriminative loss lais given by the discriminator D guaranteeing that the generated images look realistic enough, 2) classification loss lote guaranteeing that the generated images have given sensitive attributes. and 3) reconstruction loss free given by the classifier C' encourages the generator to preserve the sensitive attribute-eccluding information as much as possible.
Note that the sensitive labels are needed in the training stage of the image attribute editor G, while we only have limited annotated sensitive sensitive labels. To this end, we develop a semi-supervised learning algorithm to train the sensitive attribute classifier and image editor by making them mutu- ally promote each other. We first train classifier and image editor on the labeled data. Then, as shown in Figure 1(b), we assign the predictive sensitive labels to the unlabeled data by C.. We only select pseudo-labeled samples with confidence of (i.e., Softmax output score) above a certain threshold thr, and train the generative model G them. Afterwards, we use G to generate addi- tional images with sensitive labels, of which sagh-coofidence images are used to train the classifier
C. We repeat the above steps until there is no new high-cot gigh-confieecce data. Note that the classifier C used to pseudo-label the unlabeled data, thereby providing the generator with more annotated training data. Meanwhile, the generator G also generates additional high-quality training data for
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generated images
views
generated images
image
original Image
G
x
Xg
G
push away
2 82
X2b-1.322
x3
Figure 2: Training Flow of Fairness-awar Contrastive Learning (taking SimCLR an example). Our proposed FairCL closes the distance between representations of positive samples (i.e., the views from the same original image but have different sensitive attributes), and pushes away the distance between representations of negative samples (i.e., the views from different original images but have the same sensitive attributes).
the classifier C. In this way, the two can promote each other. Please refer Appendix for the details of the above algorithm. The discussion of
thr is in Appendix B.
Here we get an image attribute editor G, based on which we can generate an augmented dataset {(*g. .r) k=1,2.... from original dataset, even without the knowledge of the sensitive attributes of the original images. It is worth mentioning that we only use pseudo-sensitive labels in the contrastive sample generation stage, which avoids the direct impact of pscudo-label noise learning fair rep- resentation. Furthermore, the augmented dataset unbiased and balanced for sensitive attributes.
3.3
FATRNESS-AWARE CONTRASTIVE LEARNING WITH BALANCED AUGMENTATION
Based on the balanced augmented dataset generated by the image attribute editor G, our goal is to train a fair and powerful representation model. To this end, we develop fairness-awar aware contrastive learning framework, and elaborate how it works based on SimCLR. However, we emphasize that our proposed general framework is not confined to SimCLR but can be easily applied to any con- trastive learning algorithm. The key idea to define the positive samples as the contrastive samples with different sensitive attributes generated by the image attribute editor G, and define the nega- tive samples as the views generated from the different images with the same sensitive attributes. Based on it, for minibatch of contrastive image pairs { xx) 1
first generate views {(+0) 22k- 2- A. )=11),=1.22..... by data augmentation. An encoder F(-) maps the views into representations [hh2-111 A... h2 kkk
1=1,2,.., then a projection head P(-) maps h2 into an- 22k* 22 1: 2)x)) 1=1,,,...N for contrastive learning. Then we define
other representations {(E)A11 20 21
fairness-wware contrastive loss
N
exp(22 20
22 1/T)
L Fair
Σ
log Σisk exp(e 22k
(1)
/ Lipk exp( 22z /2/T)²
where T is a temperature parameter. As shown in Figure 2, a fair and effective representation model be trained on the balanced augmented dataset with the proposed fairness-aware contrastive loss.
3.4 BALANCE UTILITY AND FAIRNESS VIA FEATURE REWEIGHTING
There is often a trade-off between utility and fairness of representation (Zhao & Gordon, 2019). To balance them without target labels, we propose the feature reweighting module. Our idea to identify the sensitive attribute-dependent sub-fcatures and reweight them when computing the similarity/distance between representations in contrastive learning. Intuitively, larger weights for those sensitive attribute-dependen sub-features will result in a fairer model.
The challenge is to judge whether a sub-feature is related to sensitive attribute. Suppose that train linear classifier Cprobe to classify sensitive attributes, which takes the representations generated by the fixed encoder F.)) and projection head P(-) as input. An intuition that those sensitive attribute-depende sub-features easier activate when predicting sensitive attributes,
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and the corresponding parameters of trained classifier Cprobe will have larger absolute value. Based on it, we propose simple but effective solution. We alternately optimize the representation model F(.), P()) and an additional linear classifier probe- We can strengthen the fairness constraint by assigning more weights to those sub-features where the absolute values of corresponding classifier parameters are large. We use the soft selection which is more flexible and general. Assuming that the parameters of Cprobe is (01, .,,... Oa), where d the number of dimensions of latent representation. Then we can compute the weights (Wi, W2, .. Wa) of sub-features as:
W/ exp(a (6:))
j= exp(a 10,1)
where E I is a scaling parameter, and larger means stronger fairness constraint, Now we can balance the utility and fairness of learned representation in fairness-aware contrastive learning via the weights w as the following:
exp( ( 1222-11 W//T)
I, FairW
log
Eieer exp(22)
20 22 w/T) + Eipn exp(222 2/2 w//T)
k=1
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We validate method the following datasets: 1) CelebA (Liu et al., 2018) is dataset with over 200k facial images, each with 40 binary attributes labels. In this paper, we follow the setting of the previous works (Park et al., 2022). We select Male (m) and Young (y) as the sensitive attributes, and set Attractive (a). Big Nose (b), and Bags Under Eyes (e) as target attributes, which have the highest Pearson correlation with the sensitive attributes. Besides, to verify the performance of our method in the setting of multi-targer labels and multi-sensitive attributes, we also set (Male, Young) as sensitive attribute and (Big Nose, Bags Under Eyes) as target label. The downstream task is unknown and only 5% sensitive attribute labels are available in the training stage. 2) UTK- Face (Zhang et al., 2017) contains over 20k facial images, each with attributes labels. We first define a binary sensitive attribute Young based on whether age is under 35 not and construct a task to predict whether the facial image is Male or not. We further validate the robustness of our method to the ratio of sensitive labels and data bias on -TKeFace dataset. More details are in Appendix F.
Evaluation Metrics. The goal of FURL-PS is to learn a fair and powerful representation model. train linear classifier top
To validate the fairesss and utility of the learned representations, of the frozen representation model and then use the test performance of the classifier as a proxy for representation quality. In this paper, we use Equal Odds (EO) (Hardt et al., 2016), one of the most commonly used notion of group fairness (Dwork et al., 2012), as the fairness metric:
P5 = Y = 3) - P. Pa E Enpre
Y = w)
(4)
where Σ is the averaged sum, Y target label, Ý is predictive label given by the classifier, and s², st E S is the value of sensitive attributes. Following (Jung al., 2022), we extend EO to multi-sensitive attribute setting:
Eng D = v)
max S'ESS
(5)
where sf, o E S is the value of sensitive attributes. A smaller EO means a fairer model. Besides, we use top-1 accuracy (%) to measure the effectiveness of learned representations.
Baselines. To our best knowledge, there is no existing work focusing on dealing with the problem of FURL-PS. Therefore, we construct some powerful baselines by combining the SOTA methods to solve partially annotated sensitive labels and the advanced methods designed for fair unsuper- vised representation learning. Specifically. CGL (Jung et al., 2022) is the SOTA method to solve the problem of partially annotated sensitive attribute labels by assigning pseudo sensitive labels based on confidence. VFAE (Louizos et al., 2015) is a fair representation learning method based on variational autoencoding architecture with priors that encourage latent factors of variation

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Input
Male
RC
Female
Figure 3: Illustration of contrastive samples generated by sensitive attribute editor.
Table I: Unsupervised attribute classification results on CelebA. To evaluate the quality of rep-
resentation, we measure equalized odds (EO) and top-1 accuracy (Acc.) trained linear classifier on CelebA dataset with 5% annotated sensitive attributes. A smaller EO means fairer model. T
and S represent target and sensitive attributes, respectively.
Method
T=a/ / S=m
T=u / Smy
T=b
S=m
T-b / Smy
T=e
Sam
T=e Smy
T=bike /
T=u S=ydm
80
Acc.
EO
Acc.
EO
Acc.
EO
Acc.
EO
Acc.
EO
Acc.
B0
Acc.
EO
Acc.
CGL+VEAE
19.1
72.7
16.2
74.0
15.5
78.3
10.6
78.8
7.6
79.6
6.9
79.5
10.2
68.7
28.7
72.7
CGL+GRL
21.3
73.4
15.6
74.4
13.1
79.6
10.9
79.5
7.4
79.9
6.1
80.2
9.8
69.6
26,9
73.8
SimCLR
36.2
77.1
22.5
77.1
26.9
81.5
19.2
81.5
19.7
81,2
10.6
81.2
12.8
71.5
39.6
77.7
FalrCL (Ours)
16.8
75,3
13.1
76.9
8.4
80.0
9.2
80.3
4.2
80.8
4.5
80.5
7.8
71.3
24.5
74.1
to be independent of sensitive attribute. We implement its unsupervised version and combine it with CGL as baseline (CGL+VFAE). We also compare our method with the combination of CGL and GRL (Raff & Sylvester, 2018), which is adversarial method used for fair representations (CGL+GRL), Since there are few existing methods for fair unsupervised representation learning, we also implement some fair supervised representation learning methods which rely target labels. Group DRO (G-DRO) (Sagawa et al., 2019) is classical and powerful method for robust and fair learning by learning set of weights for different data subgroups. FSCL (Park al., 2022) learns fair representations based on supervised contrastive learning. For those 5% samples annotated with sensitive attributes, their target labels are available for G-DRO and FSCL. The CGL strategy is also used for them (CGL+G-DRO, CGL+FSCL).
4.2 CONTRASTIVE SAMPLE GENERATION EXPERIMENTS
We set Male as the sensitive attribute, and select some contrastive samples generated by the sensitive attribute editor, which is trained on CelebA dataset with partially annotated sensitive attributes, as shown in Figure 3. As can be seen, the generated contrastive samples remain most of the visual details of the original images, but have different sensitive attributes.
We next discuss the issues of proxy variables. First, for those proxy variables which have the causal/stable correlation with the sensitive attributes, e.g. Beard and Moustache, note that the sensitive attribute editor can learn the correlation between them as we expected. For example, the output female does not have beard, even if the input image is male face with beard. Unfortu- nately, for those proxy variables having the extreme spurious correlation with the sensitive attributes, e.g., Heavy Makeup and Lipstick, the sensitive attribute editor also learns it. Note that almost all male images have no lipstick (less than 1%). while most female images have lipstick (more than 80%). As result, as shown in Figure the generated female images sometimes have lipstick, even if the original image is male face without lipstick. This will result in the representation model not being able to learn information about these proxy variables and thus unable to make accurate predictions about these attributes. We would like to emphasize that this issues is open problem and, to our best knowledge, there is no existing method that can solve it without any prior. However, we find that method exhibits robustness to data bias caused by spurious correlations. Specifically, the sensitive attribute editor would not change the attributes which have the high Pearson correlation with the sensitive attributes, e.g., Big Nose, due the reconstruction loss.

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Table 2: Comparison with the methods relying on target labels on CelebA. 5% samples are annotated with sensitive attributes, and corresponding target labels are available for those target label-dependen methods, Notably, our proposed FairCI, does not rely on target labels.
Method
T=a/
S=m
T=o / Sey
T=b
S=m
T=b1
T=e/
S=m
T=e Say
T=h&e / S=y
T=o Sey
B0
Acc.
EO
Acc.
BO
Acc.
EO
Acc.
EO
Ace.
BO
Acc.
BO
Acc.
B0
Acc.
CE
27.8
77.9
19.2
17.9
20.3
82.1
14.2
82.1
16.4
82.0
11.7
82.0
9.6
71.9
36.2
77.9
CGLAG-DRO
14.2
73.8
11.3
75.3
7,9
77.1
6.2
76.3
4.5
76.9
5.1
76.7
5,3
67.2
21,9
71.4
CGL+FSCL
17.4
75.3
13.5
76.2
9.5
79.7
9.6
79.1
5.9
81.1
5.9
80.4
8.2
69.8
25.6
74.0
FairCL (Ours)
16.8
75.3
13.1
76.9
8.4
80.0
9.2
80.3
4.2
80.8
4,5
80.5
7,8
71.3
24.5
74.1
4.3
RESULTS OF FAIRNESS AND ACCURACY ON CELEBA DATASET
We report the classification results of unsupervised methods on CelebA dataset in Table 1. We use equalized odds (EO) and top-1 accuracy (Acc.) trained linear classifier to evaluate the fairness and utility of learned representations, respectively. A smaller EO mcans a fairer model. We find that the models trained via SimCLR achieves the best accuracy, but suffer from fairness issues. Our proposed FairCL. based on SimCLR improves the faimess of learned representations. FairCL outperforms other unsupervised baselines (CGL+VFAE and CGL+GRL) in terms of EO and accuracy.
We also compare our FairCL with the methods relying target labels, shown in Table 2. Notably, FairCL even outperforms semi-supervised methods in some cases. The reason may be that semi- supervised methods suffers from the issues of pseudo-label noise. Besides, CGL+G-DRO achieves excellent EO but has low accuracy due to the over pessimism problem (Hu et al., 2018).
4.4 T-SNE VISUALIZATION
To further evaluate the quality of our learned representations and ex- plain how our method works, we sample 1000 images from test dataset and visualize the represen- tations of them via t-SNE (Van der Maaten & Hinton, 2008), as shown in Figure 4. We find that both FairCI. and SimCLR can assign similar features to the images with same target labels. However, Sim- CLR also learn information of sen- sitive attributes, so that the rep- resentations given by SimCLR can also be divided by the sensitive at- tributes. In contrast, our proposed FairCL focuses on both utility and fairness by closing the distance between representations of con- trastive samples, which have similar visual information but have different sensitive attributes.
Target: Seusitive
Targut: Sensilive:
Targut: Sensitive
larget Sensitive
FairCL (Ours)
SimCLR
Figure 4: t-SNE visualization for the learned representations.
4.5 EFFECTIVENESS OF FEATURE REWEIGHTING
We provide an unsupervised way to balance the fairness and utility of learned representation by feature reweighting. To analyze the effectiveness of feature reweighting, we set the scaling param- eter a different values, and train the representation models. The performance of linear classifier in terms of EO and accuracy is reported parameter can yield fairer model but with lower accuracy. This is in line with our expectations, since the feature weights will be hard if the scaling parameter a is large, and then the sensitive attribute-dependent features will have greater impact on the similarity/ddstancee calculation.
Table 3. As can be seen, we find that a larger scaling
4.6 COMPATIBILITY WITH CONTRASTIVE LEARNING ALGORIHTMM
Our proposed faimess-awarc contrastive learning framework is general and flexible that can be used for any contrastive learning algorithm. We have shown the effectiveness of SimCLR-based FairCL in the previous subsection. To further illustrate the compatibility of FairCI., apply it BYOL (Grill
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Table 3: Effectiveness of feature reweighting on CelebA dataset. We train the representation
models with different scaling parameter to analyze the effectiveness of feature reweighting
Swm
/=y
S=m
T=b Smy
/ S=m
Tac/ Sey
T=Mee/ Smy
T=a
/ayylm
EO
Acc.
E0
Acc.
E0
Acc.
E0
Acc.
E0
Acc.
E0 Acc.
E0
Acc.
E0
Acc.
a=
0.5
16.8
75.3
13.1
76.9
8.4
80.0
9.2
80.3
4.2
80.8
4.5
80.5
7.8
71.
24.5
74.1
2.0
14.6
74.2
11.5
76.3
6.2
79.1
6,9
79.3
3.6
80.5
2.8
80.1
5.9
70.4
22.3
72.9
Table 4: Compatibility with contrastive learning on CelebA dataset. We apply our general
framework to BYOL and denote it as FairCL*. The experimental setup is the same as before.
T S=m
T=a
Smy
T=b / S=m
T=b
S=y
T=e / Smm
T=e/S=y
T=bde / S=y
T=a / S=y&mm
Method
F0
Acc.
EO
Acc.
E0
Acc.
EO
Acc.
B0
Acc.
EO
Acc.
RO
Acc.
FO
Acc.
BYOL
38.8
77.9
77.9
28.2
818
20.6
818
22.3
81.7
12.9
81.7
14.6
72.2
41.5
77.9
F
76.2
77.1
13.7
80.8
11.0
80,9
7.2
81.0
6.8
80.9
9.9
71.6
28.6
74.9
et al., 2020), a widely used contrastive learning method without negative samples. Due to the ab- sence of target labels, negative sample-based methods (e.g.. SimCLR) may incorrectly push the rep- resentations of semantically similar samples farther away, which may lead to compromised accuracy on downstream tasks. BYOL overcomes this problem by not using negative samples. Therefore, as shown in Table 4, BYOL achieves excellent accuracy. However, it also suffers from fairness issues. Notably, F improves EO over it while keeping comparable accuracy.
4.7 ROBUSTNESS TO RATIO OF ANNOTATED SENSITIVE LABELS AND DATA BIAS
To further validate the robustness of our proposed method to ratio of annotated sensitive labels and un- known data bias, run different methods on UTK- Face dataset. We set Young as the sensitive attribute, and the target label is Male. Only 5% samples notated with sensitive attributes. Besides, the dataset unbalanced, where the Young group has 65% fe- male data and 35% male, while another sensitive group has the opposite gender ratio. As can be seen in Fig- ure 5, our proposed FairCL/FairCL* improves fairness compared with SimCLR//BOL, while keeping high ac- curacy. Our method outperforms other unsupervised methods and even achieves comparable performance with the target label-dependent methods (CGL+G-DRG and CGL+FSCL). in terms of the trade-off between ac- curacy and EO, which illustrates that method is bust to annotation ratio and data bias. More experiments with various settings are in Appendix E.
Accuracy (%6
24
CE
CGL-VFAE
BYOL
CGL+G-DRO
CGL+GRL
FairCL (Ous)
CGI.F5CC
SinCER
TairCT (Dus))
EO
Figure 5: Robustness to ratio of sensitive labels and data bias on UTKK-Faee A rep- resents methods relying on target labels.
CONCLUSIONS
In this paper, we investigate novel and practical problem of which the goal is to learn a fair and powerful representation model with no target label and limited sensitive attributes. To solve this problem, we develop general contrastive (earning-based framework FairCL consisting of two stages: contrastive sample generation and fairness-aware contrastive learning with feature reweight- ing. Extensive experiments show that our proposed method can yield a fair representation model even with limited sensitive attributes and imbalanced data.
Limitations and Future Work. The main limitation that our work relies on the quality of the generated images. The effectiveness of our method on more challenging domains remains to be explored due the potential lower-quality generations. We leave it as an open problem for future work. In practice, we suggest the users choose the appropriate generative methods according to the datasets and tasks. Since our proposed fairness-aware framework is compatible with different generative methods, as shown in Appendix E. Moreover, we believe that more effective generative methods can further help improve the quality of representation model trained by our method.

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ACKNOWLEDGMENT
This work was supported by the National Key Research & Development Project of China (2021ZD011040)) 10400), the National Natural Science Foundation of China (U19B2043, U19B2042, U20A20387, 61976185, 62006207, 62037001), Project by Shanghai Al Laboratory (P22KS00111) Program of Zhejiang Province Science and Technology (2022C01044), the StarryNight Sci- ence Fund of Zhejiang University Shanghai Institute for Advanced Swudy (SN-ZJU-SIAS-00100) Academy Of Social Governance Zhejiang University, and the Fundamental Research Funds for the Central Universities (226-2022-00142, 226-2022-00051)
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A SEMI-SUPERVISED ALGORITHM FOR LEARNING CLASSIFIER AND GENERATOR
The detailed presentation of the method for training generator with limited sensitive attribute la- bels is shown in Algorithm 1. The semi-supervised algorithm for learning classifier and generator consists of three stages: warm-up training (line 1), dataset extension (lines 2 to 13), and follow-up training (line 14).
Algorithm 1 Semi-supervised Algorithm for Learning Classifier and Generator
Input: Dataset with sensitive labels Dr, unlabeled dataset Du., threshold thr E (0, 1), initialized classifier C, and initialized generator G.
1: Train classifier C and generator G labeled dataset Di-
2: Initialize classifier's training set D = Dr, generator's training set D Di, and candidate
set Deand D..
3: repeat
4: Assign sseudo-sensitive labels samples in candidate set Deand = Du using classifier C.
5: Construct a temporary dataset D Deonf consisting of samples in Deand whose confidences (Softmax scores) are higher than threshold thr.
6:
Update (extend) generator's ' training set D DG U Deonf-
7:
Update (reduce) candidate set Doand = D cand Doons-
8: Train generator G on dataset D
9:
Edit the sensitive attributes of the samples in dataset DC using generator G to construct temporary dataset D Dgen-
10:
Construct temporary dataset D' f consisting of samples in Dgen whose confidences (Soft- max scores) are higher than threshold thr. Update classifier's s training set D = D D Donnf"
11:
12:
Train classifier C' on dataset De- until the temporary dataset D Dcomf= =
Train classifier C and generator G dataset D and D respectively. return classifier C' and generator G.
15:
B DISCUSSIONS OF HYPER-PARAMETER
B.1
thr
For classifier and generator. The pseudo-labels given by the classifier may be wrong and not all of the generated samples are high-quality. As a result, there may be some label noises. When assign a pseudo-label to a sample, intuitively, high Softmax score means less probability of being classified incorrectly. Therefore, mitigate the label noises, introduce the parra--parmmetr thr to decide whether an image could be used as training sample for classifier and generator.
For representation model. Before we perform fairness-aware contrastive learning, we first prepare contrastive sample pairs using the trained generator. Then we remove some low-confidence gener- ated samples, since there are few low-quality generated samples with wrong sensitive attributes, which may introduce bias in the training process of the representation model. Specifically, we com- pute the average Softmax score for each pair contrastive samples, and the sample pairs with low average Softmax scores (less than Uer) will be removed.
Effeet thr. To explore the effect of hyper-parameter thr, we ablation studies by setting thr to different values. As shown in Table 5, we can observe that the model's Equal Odds (EO) gradually decreases with the increase of thr. This experimental phenomenon is consistent with our expectation, since large values of thr guarantee the correctness of the sensitive attributes of the training samples. However, the increase of thr results in a slight decrease in accuracy, which may be due to that the number and diversity of training samples is reduced.
How to choose thr? There is no criterion to choose thr, since the datasets, generative models, randomly sample from
training algorithms and requirements are not invariant. However,
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Table Ablation studies of different values hhyer-parameetrr thr on CelebA dataset. We Male as sensitive attribute and Attractive as target label.
Method thr
FairCL
SimCER
0.99
0.90
0.80
0.00
1
Ratio of removed data (%)
28.4
20.0
11.6
0.0
0.0
Accuracy (%) (1)
75.0
75.3
75.8
76.7
77.1
Equal Odds (EO) (J)
15.8
16,8
18.0
20.2
36.2
the data at different thr to assess the quality of the data at different thr. In this paper, we set thr 0.9 to balance the fairesss and accuracy. In practice, we recommend that the users who are very concerned about faimess set the thr to a large value.
B.2 HYPER-PARAMETER
We introduce the hyper-parameter a to control the trade-off between accuracy and fairness for our proposed algorithms. As shown in Table 3, a larger scaling parameter can yield fairer model but with lower accuracy.
B.3 HYPER-PARAMETER
Hyper-paramette parameter т is a temperature parameter, which is widely used in contrastive loss computation for sharper distribution of the Softmax output. In this paper, follow the setting in (Chen et al., 2020a) and set r 0.5.
C EXPERIMENTS OF FAIRNESS-ACCURAS TRADE-OFFS
The classification results of our proposed method and the compared unsupervised baseline methods are reported in Table There are two main metrics focus on: fairness (EO) and accuracy. From the perspective of multi-objective optimization, we can observe that our FairCL. strongly Pareto- dominates CGL+VFAE and CGL+GRL, since FairCL outperforms them in terms of both fairness (EO) and accuracy. However, we find that FairCL achieves the best performance in terms of fair- ness, while SimCLR achieves the best accuracy. To further compare these two methods terms of the trade-off between fairness and accuracy, accuracy during the training process of the linear classifier, as shown in Figure 6. Obviously, our method has lower EO value when the two achieve the same accuracy, which means that our FairCI. has a better trade-off between accuracy and fairness.
plot the trade-off curves according to the EO and
D BEYOND FAIRNESS: GENERAL BIAS MITIGATION ON NON-FACIAL DATASET
To explore how our proposed framework performs on general bias mitigation, we run different un- supervised algorithms on a non-facial dataset, Dogs and Cats (dog, 2013), which is commonly used in the previous debiasing studies.
Dataset. Dogs and Cats dataset consists of dog and cat images. Kim et al. (2019) provide additional annotations for partial images about whether the color of dog/cat is dark or not. We set color as the sensitive attribute, and the task is to predict if the image is cat dog. We construct biased training set consisting of 2,000 bright cat images, 4,000 bright dog images, 4,000 dark cat images, and 2,000 dark cat images. The Pearson correlation coefficient between target label and sensitive attribute is -0.33, so that the model may learn the spurious correlation between species and colors. We assume that 10% of the samples annotated with sensitive attributes. The test set is balanced (unbiased) and consists of 2,400 images. We use the same experimental setups (training strategies, before.
models, and papemeparmmttess)
Experimental Results. We randomly select some generated contrastive sample pairs. as shown in Figure 7. We can observe that the generated contrastive samples remain most of the visual details
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SinC TiHCL
SamCLR FaICL
SinCui PainLL
610
Accumcy
0.2
180
25
30
(a) T=Aitractive, S=Male
(b) T=Big Nose, S=Male
(c) T=Bags Under Eyes, S=Male
714
NiFCL
SimCLR
46.-
PeFCL
SInCUR
765
Accuracy (5)
Accuracy (8a)
Accuracy 10.
30
(d) T=Attructive, S=Young
(e) T-Big Nose, S=Young
(I) T-Bags Under Eyex, S=Young
Figure 6: Fairnss--accuracy trade-off curves on CelebA dataset. Our FairCL achieves a better trade-
off between accuracy and fairness.
Input
Dark
Bright
Figure 7: Illustration of contrastive samples generated by sensitive attribute editor.
of the original images, but have different sensitive attributes (colors). We report the classification results in Table 6. Our proposed FairCI. improves fairness compared to SimCLR with only slight drop in accuracy. Moreover, FairCl. outperforms all baseline methods in terms of both fairness and accuracy, The above experimental results illustrate that our FairCL can prevent representation models from learning spurious correlations.
Table 6: Unsupervised classification results on Dogs and Cats dataset. To evaluate the quality of representation, measure equalized odds (EO) and top-1 accuracy (Acc.) of trained linear classifier on Dogs and Cats dataset with 10% annotated sensitive attributes. smaller EO means fairer model. T and S represent target and sensitive attributes, respectively.
T=species, S-color
(J)
Acc. (f)
CGL+VFAE
10.3
85.9
CGL+GRI.
9.7
86.6
SimCER
12.4
88.1
FairCL (Ours)
8.2
87.5
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Input
Editing Direction (+)
Editing Direction (-)
Figure 8: Illustration of contrastive samples generated by sensitive attribute editor on UTK-Face dataset. The generative model is trained by HFGI. (+)((-) represents that the samples are generated based on the input images plus/minus sensitive attribute-related (race-related) direction.
E COMPATIBILITY WITH GENERATIVE FRAMEWORKS
Our proposed fairness-aware contrastive learning is compatible with differrntt generative meth- ods. The descriptions of the contrastive sample generation in the previous section are based on AuGAN (He et al., 2019). To verify the compatibility, we use another generative method, HFGI (Wang et al., 2022), to generate contrastive sample pairs.
Contrastive Sample Generation based on HFGI. We design simple pipeline of contrastive sam- ple generation based on HFGI, which consists of three steps: generator training, classifier training, and image editing. First, train inverse GAN using the same experimental settings HFGI. In this process, we do not need to use any data annotations. Secondly, we train a robust classifier using JTT (Liu al., 2021) as the training strategy with limited sensitive attributes. The trained classi- fier is used to assign pseudo-sensitive labels to the unlabeled images. Finally, we get the editing direction w.r.t. sensitive attribute based on InterFaceGAN (Shen al., 2020). Then we can generate contrastive sample pairs based on the editing direction. We recommend the readers to read the above references for implementation details.
Experimental Setup. We use UTK-Face the dataset. We set race as the sensitive attribute. For convenience, we consider a binary setting, and put white people in one group and other races in another. We would like to state that this grouping is only due to the large proportion of white people in the dataset. The task is to predict the gender for given image. We construct three unbalanced settings where the Pearson correlation coefficients of race and gender in the training dataset are -0.2, -0.4 and -0.6, respectively. The size of training set is 10,000 in each setting. We also construct balanced (unbiased) test set with 3,200 images for each setting. We assume that 10% of the samples are annotated with sensitive attributes. To demonstrate the compatibility of our framework contrastive learning algorithms, we compare BYOL and FairCI.* on this dataset, neither of which requires negative samples. Other experimental setups are the same as before.
Experimental Results. We randomly select some generated contrastive sample pairs, as shown in Figure 8. We report the classification results in Table 7. We observe that our proposed FairCL improves BYOL terms of both fairness and accuracy in three settings, which illustrates that the representation models trained by our framework is more effective and fairer. We also find that the visual quality of the generated images is not as high as we would like, and the racial changes in the edited images are not very obvious. However, the classification results of our method are still greatly improved compared with the baseline, which means that a good representation model can be learned by our proposed framework even the quality of the generated samples is not very high.
Discussion. Our proposed framework is based on the generation techniques to prepare contrastive sample pairs for fairness-aare contrastive learning. The above experimental results demonstrate the compatibility of our framework to different generative methods. Moreover, we would like to em- phasize that the goal of our study is to lear fair and effective representations, and non-high-qualln generative samples do NOT mean bad representations. At last, we believe that the applicability of method will get stronger and stronger with the development of generation techniques.
17

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Published as a conference paper at ICLR 2023
Table 7: Unsupervised classification results UTK-Faae dataset based HFGI generative method. The sensitive attribute is race, and the task is to predict the gender. We consider three settings with
different Pearson correlation coefficients of the training set. The test set balanced w.r.t. both race
and gender. Our FairCL" improves BYOL in terms of fairness and accuracy in all settings.
T=gender, S=race
Pearson=-0.2
Pearson=-0.4
Pearson=-0.6
Method
EO (L)
Acc. (1)
(O)
Acc. (1)
EO (J)
Acc. (f)
BYOL
2.1
89,4
8.7
87.5
13.1
86.4
FairCL* (Ours)
1.7
90.6
5.6
89.6
8.9
89.7
Improvement
-0.4
1.2
-3.1
2.1
-4.2
3.3
Table 8: Overview of Experimental Settings. Pearson correlation coefficient measures the linear correlation between target variables and sensitive attributes. For CelebA dataset, we do not use Wearing Lipstick, Heavy Makeup, Mustache, or No Beard as the target variables, since their sample sizes of minority groups less than 50 (<0.25%) in test set.
Generative method
Sensitive
Target
Pearson coefficient
Ratio of labeled data
Dataset
attribute
variable
Attractive
-0,40
Male
Big Nose
0.37
Bags Under Eyes
0.30
CelebA
AtGAN
5%
Attractive
0.39
Young
Big Nose
-0.29
Bags Under Eyes
-0.33
Wearing Lipstick
-0.79
Heavy Makeup
-0.67
Male
Mustache
0.25
No Beard
-0.52
CelebA
Wearing Lipstick
0.25
Heavy Makeup
0.25
Young
Mustache
-0.14
No Beard
0.12
AttAAN
Age
Gender
-0.30
5%
-0.20
UTK-Face
HFGI
Race
Gender
-0.40
10%
-0.60
Dogs and Cats
AtGAN
Color
Species
-0.33
10%
F DETAILS OF EXPERIMENTAL. SETUP
Overview of Experimental Setup. The overview of our experimental settings is shown in Table 8. For CelebA dataset, the sample sizes of Wearing Lipstick, Male, Heavy Makeup, Male, Mustache, Female, and No Beard, Female test set are 47, 22, O, and 10, respectively. All of them have a fraction less than 0.25% in the test set,
do not use these attributes in experiments.
Implementation Details. We resize the images of CelebA and UTK-Face to 128x 128, and use a 5-layer CNN (Krizhevsky al., 2017) the encoder of generative model. Besides, the decoder also has 5 layers. Since the quality of some generated samples may not be good enough, we select images based confidence. Specifically, we the trained classifier predict the sensitive attributes of the generated images, and then remove some low-confidence samples whose Softmax output scores are lower than thr 0.9. Besides, further improve the quality of training data, we use high-confidence of original images instead of corresponding generated samples. We use the same random data augmentation strategies as (Chen et al., 2020a). The projection head P(.) is only used in contrastive learning, and then we remove it. We use the ResNet-18 (He et al., 2016) as encoder model and a MLP as projection head, and train them via weighted Tairness-wware contrastive loss for 100 epochs. Afterwards, train linear classifier top of the frozen representation given by encoder F(.) for 10 epochs on the training dataset.
18


Reviews:
[{"review_id": "qYaSAVxoxf", "reviewer": "Reviewer_G4GB", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "Traditional representation learning methods (for example, contrastive learning), while making significant progress, have potential fairness issues. This paper proposes training the image attribute editor to generate contrastive sample pairs for each sample in the original dataset, which share the same visual information except for sensitive attributes (e.g., male and female). To enable the model to learn powerful and fair representations, it reduces the distance between representations of contrastive sample pairs. The key idea is to first build the debiased training set and then learn the debiased representation. The proposed method outperforms existing unsupervised learning methods in terms of classification accuracy and fairness, according to experimental results on the CelebA and UTK-Face datasets.\n\n", "summary_of_the_review": "Although this work has some merits, I am concerned about the experimental evaluations and the proposed method's real-world applicability.", "strength_and_weaknesses": "Advantages:\n- It is worthwhile to investigate the problem of fair unsupervised representation learning with only partially annotated sensitive attributes.\n- The paper is well written.\n- The proposed FairCL framework is generally sound, and experimental analysis could validate the framework's effectiveness to some extent.\n\nDrawbacks:\n- My main concern is that the proposed method is limited to face recognition-based applications. It is now possible to generate contrastive samples for face images thanks to the advancement of GAN-based techniques. However, doing so for other applications is challenging. Without high-quality augmentations, it is doubtful that this method can be applied in other applications.\n- Another major concern is that the authors only show one point on the fairness-accuracy trade-off curve. The majority of existing literature assesses the effectiveness of fairness mitigation algorithms using a fairness utility curve to determine which algorithm achieves the best fairness accuracy trade off. It would be difficult to determine which of the two is superior if only one point on the curve was considered: 1) EO: 39.6, and ACC: 77.7, and 2) EO: 24.5, and ACC: 74.1.\n- The evaluation is limited to the two datasets of the face recognition tasks.\n- An important baseline is missing. That is, we first train the sensitive attribute classification model using the partial sensitive attribute annotations. Following that, this model can be used to generate pseudo sensitive labels for other unlabelled samples. This method may not work well with difficult real-world datasets. However, for the two datasets used in this study, CelebA and UTK-Face, even a low ratio of labeled sensitive labels (e.g., less than 5%) are sufficient to train high-quality sensitive attribute classification models. The main reason is that the sensitive attribute annotation (male and female) task is much easier than the underlying task, such as Attractive.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "dGZo84I09c", "reviewer": "Reviewer_MhQc", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "The author aims to bring attention to a known problem by giving it a formal name FURL-PS and definition, and proposes a practical solution, FairCL/FairCL* which is a novel combination of known theory and algorithms previously not having been applied to this particular problem of ML fairness. In particular, the paper demonstrates how to tackle the problem of training a face image encoder with a good ratio between the equalized odds and the face attribute classification accuracy, given a dataset partially annotated with sensitive labels, such as gender and age, and without the use of the target labels of the downstream tasks (in this case, face attribute classification). The paper is backed with appropriate model performance comparison tables and graph to show the effectiveness of the proposed solution FairCL/FairCL* along with some of its limitations.", "summary_of_the_review": "I recommend to accept this paper. Despite some lingering doubts mentioned before, the theory seems sound, and the empirical results seem comprehensive and promising. The paper is coherently written with clear language backed by appropriate tables and figures to assist easier reading and understanding. The proposed method is novel and tackles an important and practical ML Fairness problem faced in the real world. ", "strength_and_weaknesses": "Dealing with a dataset partially annotated with sensitive labels is a known and common problem in the real world. However, as far as I know, this paper seems to be the first to give the problem a formal name FURL-PS to be able to refer to it with an ease. In addition, the choice of using AttGAN for the generative model is clever because AttGAN was already demonstrated in the original paper with the same CelebA dataset to modify only the intended face attribute. So, in this case, just a sensitive face attribute can be modified according to the already approved paper's demonstration. This paper demonstrated the effectiveness and limitation of using negative samples generated by an image editor with two different versions of the solutions FairCL and FairCL*. It seems like an extra mile the authors took that made the paper's findings even more interesting and useful for the reader. The theory used to balance between utility and the fairness using feature re-weighting in the absence of the target labels, is also simple and clever. The experiments themselves seem to be a high quality in that the authors chose downstream task face attributes that have high Pearson correlation with the sensitive attributes and in that the authors honestly wrote the limitation of the proposed solution in the presence of the \"extreme spurious correlation\" like in the case of Heavy Makeup and Lipstick. Figure 5 is also very helpful to better understand the effectiveness of the proposed model compared to other prior arts.\n\nNevertheless, there were some lingering questions/doubts about the approach described. The process of training the image editor G and the sensitive label classifier C with unlabeled data feels like a self-feeding/self-reinforcing loop that can introduce some bias. There wasn't much discussion about this to satisfy my doubts about the proposed model's fairness limitation that may come from this self-feeding training loop. It would have been also interesting if the author listed the Pearson correlation numbers of the face attributes that the author defined as \"extremely spurious correlation\" vs \"spurious correlation\" in order to translate the potentially qualitative and intuitive description into a more quantitative description.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "G8rKcuAsvo", "reviewer": "Reviewer_Vv8C", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "In this manuscript, author attempt to employ the supervision of limited sensitive labels in images for learning fair representations. To address this new task, they refer to the idea of contrastive learning and propose a novel fairness-aware framework. Specifically, to ensure this fairness in contrastive learning, author adopt a generator to obtain some positive samples with various sensitive attributes, which could avoid the effect of sensitive attributes to ensure impartial learning process.", "summary_of_the_review": "The highlight of this paper falls on their motivation and solution, which has well practicability and worth to generalize. It provides a new direction to implement unbiased and fair representation learning under the limited annotated information.Besides, although the number of experiments is enough, the related analysis is barely satisfactory. Some experiments result still need to further analysis. ", "strength_and_weaknesses": "Strength:\n1)The first strength of this article is the proposed new problem: FURL-PS, which aims to settle the key issue that sensitive attribute label is high cost to obtain and even suffer privacy policy. It has well practicability and worth to generalize.\n2)Besides, a major strength in this method is to implement the dataset adjustment to adapt the contrastive learning in self-supervised manner, thereby design a general framework FairCL. The design solution for the absence of sensitive label is also technically feasible to generate some positive samples that support the fair representations learning. In this case, after identifying the qualities of representations, it makes sense to implement this method and realize the process of fair learning.\n3)The corresponding experiments in two datasets are enough to validate its effectiveness and generalizability.\n\nWeakness:\nAccording to my understanding, the performance of this method relies heavily on the quality of generated images as positive sample, which is implemented by off-the-shelf framework AttGAN. In order to verify the generalization of your method, I think you should further discuss the effect of different frameworks of image generation. If possible, you could also provide some failure cases to observe the stability of your model.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "-_aNm_YPeR", "reviewer": "Reviewer_kKgZ", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "The paper proposes a variation of contrastive learning that learns fair representation with partially annotated sensitive attribute labels. It combines several components together to achieve the goal: generative model that can generate \"counterfactual\" examples with different sensitive labels for making the positive and negative samples for contrastive learning, pseudo-label prediction model to estimate the sensitive attributes, and feature re-weighting scheme for balancing utility and fairness. The resulting method is shown to achieve positive results on benchmark dataset for (unsupervised) learning fair representations with partially annotated sensitive labels. ", "summary_of_the_review": "As mentioned above, while the problem setting seems to be new (but, it is also inspired by recent work (Jung et al, 2022) and is not very hard to think of) and there are some positive aspects in the paper, I think the overall method simply combines existing methods (which undermines the novelty) and the description of the method is not rigorous/general enough for a publication at ICLR. \n\n**** Post-rebuttal\nI think some limitations still exists since the proposed method looks highly dependent on the examined dataset regarding face images, for which synthetic data is relatively easy to generate via GAN. But, the proposed problem setting indeed can be regarded as novel and the paper also has some value of tacking it for the first time. The experimental results for the considered dataset is promising, too, so I raised my score to 6. I think the authors need to at least describe the limitation of their method as well. ", "strength_and_weaknesses": "Strength:\n- As mentioned above, the method combines multiple methods to achieve the goal, which is to learn fair representation with partially annotated sensitive label and without target label. It seems to be the first work to consider this specific setting. \n- The generative model example and t-sne visualization provides necessary qualitative results. \n- The proposed method is compatible to several contrastive learning methods. \n- The method can generate similar quality of fair representation compared to the methods with target labels. \n\nWeakness:\n- There are many hyperparameters (*thr* for generative model, $\\alpha$, $\\tau$), and the effect of them is not considered or analyzed. There is only one table on $\\alpha$. Particularly, I think the effect of *thr* should be very important since the generative models are typically sensitive to the hyperparameter choice. \n- The method is highly dependent on the GAN-based generative model, and when the dataset is not face-related images like CelebA or UTKFace, it is not clear how the method would perform. \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster