FairLISA: Fair User Modeling with Limited Sensitive Attributes Information
Authors: Zheng Zhang
Qi Liu
Hao Jiang
Fei Wang
Yan Zhuang
Le Wu
Weibo Gao
Enhong Chen
Venue: NeurIPS 2023 poster (2023)
Keywords: fairness
user modeling
TL;DR: We focus on fair user modeling in limited sensitive attributes situations and propose a novel FairLISA framework, which can efficiently utilize data with known and unknown sensitive attributes to facilitate fair model training.

Abstract: User modeling techniques profile users' latent characteristics (e.g., preference) from their observed behaviors, and play a crucial role in decision-making. Unfortunately, traditional user models may unconsciously capture biases related to sensitive attributes (e.g., gender) from behavior data, even when this sensitive information is not explicitly provided. This can lead to unfair issues and discrimination against certain groups based on these sensitive attributes.  Recent studies have been proposed to improve fairness by explicitly decorrelating user modeling results and sensitive attributes. However, most existing approaches assume that fully sensitive attribute labels are available in the training set, which is unrealistic due to collection limitations like privacy concerns, and hence bear the limitation of performance. In this paper, we focus on a practical situation with limited sensitive data and propose a novel FairLISA framework, which can efficiently utilize data with known and unknown sensitive attributes to facilitate fair model training. We first propose a novel theoretical perspective to build the relationship between data with both known and unknown sensitive attributes with the fairness objective.  Then, based on this, we provide a general adversarial framework to effectively leverage the whole user data for fair user modeling. We conduct experiments on representative user modeling tasks including recommender system and cognitive diagnosis. The results demonstrate that our FairLISA can effectively improve fairness while retaining high accuracy in scenarios with different ratios of missing sensitive attributes.

FairLISA: Fair User Modeling with Limited Sensitive Attributes Information
Zheng Zhang 1.2
Qi Liu Hao Jiangl 1.2 Fei Wang 1.2
Yan Zhuang 1.2
Le Wu Weibo Gao 1.2
Enhong Chen 1.2
1: Anhui Province Key Laboratory of Big Data Analysis and Application
University of Science and Technology of China
2: State Key Laboratory of Cognitive Intelligence
3: Hefei University of Technology
(zhangzheng, jianghao0728. wf314159 zykb, weibogao) @mail ust.cstccooo www.uuuu..cc..... edu {qiliugl, chenehhuustc edu. en;
lewu. uww.@gmail.c......m.oom. com
Abstract
User modeling techniques profile users' latent characteristics (e-g., preference) from their observed behaviors, and play a crucial role in decision-making. Un- fortunately, traditional user models may unconsciously capture biases related sensitive attributes (c.g., gender) from behavior data, even when this sensitive infor- mation is not explicitly provided. This can lead unfair issues and discrimination against certain groups based on these sensitive attributes. Recent studies have been proposed improve fairness by explicitly decorrelating user modeling results and sensitive attributes. However, most existing approaches assume that fully sensitive attribute labels are available in the training set, which unrealistic due collection limitations like privacy concerns, and hence bear the limitation of performance. In this paper, we focus on practical situation with limited sensitive data and propose a novel FairLISA framework, which can efficiently utilize data with known and unknown sensitive attributes to facilitate fair model training. We first propose novel theoretical perspective build the relationship between data with both known and unknown sensitive attributes with the fairness objective. Then, based on this, we provide a general adversarial framework to effectively leverage the whole user data for fair user modeling. We conduct experiments on representative user modeling tasks including recommender system and cognitive diagnosis. The results demonstrate that our FairLISA can effectively improve faimess while retaining high accuracy in scenarios with different ratios of missing sensitive attributes.
Introduction
User modeling is a fundamental task in many applications (e.g., recommender system), which aims to infer latent characteristics through amalyzing users' behavioral information, such capability and preference [56]. The results from user modeling serve as an important basis for decision-making,
which might affect users' lives to extent 11 [5] However, recent studies have demonstrated that biases related to sensitive attributes (e.g., g., gender, race) can be unconsciously captured from behavior data, even when this sensitive information not explicitly provided. This can lead to unfair user modeling results 33 23 45). For example, college admissions based on user modeling may underestimate underrepresented demographic groups [48]. Career recommendation shows apparent
gender discrimination even for equally qualified men and women [19]. As such, it is crucial to consider fairness issues and ensure that users with different sensitive attributes are treated similarly.
"Corresponding Author
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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Following previous studies 3 23 45], the faimess requirement for user modeling in this paper is that the results should not expose any sensitive information. This requirement can be formalized as minimizing the mutual information between user modeling results and sensitive attributes [2]. Along this way, many approaches have heen proposed, such as constraint optimization [1, adversarial learning 3 23 45 20 and regularization methods (4 42], among which adversarial methods show
their theoretical elegance and receive widespread attention.
While many methods have shown promising results, most of them require explicit sensitive attributes to guide fair model training. However, in real-world scenarios, users are not always willing to share sensitive information due to privacy concerns. For example, only 14% of teen users expose their complete profiles on Meta [31]. The limited sensitive attributes situation means that only a limited amount of data with known sensitive attributes can participate in model training, resulting in insufficient training data problem for existing methods. The most practical solution is to utilize data with unknown sensitive attributes to facilitate model training. Nevertheless, several obstacles must be addressed, including: 1) Efficient data utilization: Currently, some researchers have utilized data with unknown sensitive attributes by predicting missing sensitive labels based on data with known sensitive attributes [5]. However, the accuracy of these predicted labels cannot be guaranteed, especially in extremely limited sensitive situations, which results in suboptimal data utilization. 2) Theoretical guarantee: The fair goal in our paper is to minimize the mutual information between the sensitive attributes and the user modeling results. When utilize data with unknown sensitive attributes to improve fair user modeling, how can we establish a good theoretical guarantee? 3) F Framework generalization: Several fair adversarial methods have been introduced to handle data with known sensitive attributes 3 23 45 20 Each method has its advantages and disadvantages in different scenarios. In this case, we believe that general framework should be proposed that can incorporate data with unknown sensitive attributes into existing fair adversarial methods.
To address these obstacles, in this paper, we propose FairLISA, a general framework for Fair user modeling with LImited Sensitive Attributes, which can efficiently utilize data with known and unknown sensitive attributes to facilitate fair model training. We first propose novel theoretical perspective to build the relationship between two types of data and the fair mutual information objective. Specifically, establish connection between data with unknown sensitive attributes and the faimess goal through shannon entropy, while data with known sensitive attributes and the fairness goal are connected through cross-entropy. By doing so, can directly leverage user data with unknown sensitive attributes without the need for predicting missing attributes, reducing information loss caused by predictions and ultimately increasing the utilization rate of data with unknown sensitive attributes. Then building on this foundation, we propose an adversarial learning approach to effectively leverage the entire user data for fair model training. Since existing fair adversarial approaches requiring fully known sensitive data can be interpreted from a mutual information perspective K 23 45)
FairLISA can easily generalize most existing adversarial methods and expand them to limited sensitive attribute situations. Finally. to validate the effectiveness of our proposed FairLISA framework, we conduct extensive experiments on two representative user modeling tasks including the recommender system and cognitive diagnosis. Empirical results show that our framework can enhance fairness with satisfactory accuracy performance, even in scenarios with different ratios of missing sensitive attributes. The main contributions of this work are as follows:
Efficient Data Utilization and Theoretical Guarantee. We provide a novel theoretical per- spective to build the relationship between data with known and unknown sensitive attributes with faimesss objective. This allows for the effective leveraging of data with unknown sensitive attributes, reducing information loss caused by predicting missing attributes.
Framework Design and Generalization. We propose general framework, FairLISA, which can expand most existing fair adversarial methods to limited sensitive situations.
Experimental Evaluations. We conduct experiments two representative user modeling tasks (i.e., recommender system, cognitive diagnosis) with six representative models and two datasets, the results demonstrate the effectiveness and robustness of FairLISA.
2
Related work
User Modeling. User modeling fundamental task that captures users' useful potential char- acteristics, such as capability, preference, and so on [4 [56 28 50). It has been applied in various

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applications. For example, based on user capability fitting, researchers employed cognitive diag- nosis [30 9 to model student proficiency 4
30); based on user preference mining, researchers applied collaborative filtering [39 for mining user interest preferences to different tasks including
news recommendation 26), social network [4 Most of the existing user modeling methods focused
27 29). However, as the results directly affect the opportunity of users [32 54 555, it can easily lead to unfair outcomes. In this paper, we explore fairness-aware user modeling, which aims to ensure fairness during the user modeling process.
on getting more precise results 4
30
16
15
Fairness in Machine Learning. Fairness in machine learning can be divided into three categories: (1) individual fairness, which requires similar individuals be treated similarly 7 [ (2) group fairness, which requires different groups divided by sensitive attributes should be treated similarly [35
13: ; (3) max max-min fairness, which aims to maximize the utility of worst-off group [37] In this work, we focus on group fairness since it can measure how the underrepresented group is treated.
In group fairness research, many fairness metrics and unfairness mitigation techniques had been 38
23 43 [0 (2). For example, Avishek et al. [3] discovered the learned user representations would capture biases of sensitive information and proposed to minimize the mutual information between representations and sensitive attributes through an adversarial framework. Li al. 23 also proposed this goal from the causal perspective and provided two similar adversarial strategies. Wu et al. 45 argued the user-centric structure would expose users' sensitive attributes and proposed an adversarial framework from a graph perspective to obtain more efficient results.
proposed [62]
36 45
47
3
Despite their ability to improve fairness, all of these methods are based on enough user sensitive attribute information. While in many real-world applications, it is difficult collect such data since users may care about privacy and are reluctant to divulge their sensitive information [31]. The lacking of sensitive information challenges most existing solutions. Exploring fair models with missing sensitive attributes is an important and challenging issue, and it is still under-explored. There are only a few works in this direction. One branch of approaches investigates fairness without sensitive attributes. For example, some researchers achieved this via solving a max-min problem [25] 14] 18]
However, they were only effective for max- min fairness (37), which was different from our group fairness goal. Some researchers tried to solve this problem with the help of related features [51 [3
However, obtaining such related features is not always feasible practical applications, thereby limiting the utilization of these methods. The other branch explores improving fairness in limited sensitive attribute situations, Dai al. (5] proposed FairGNN, the main idea was to predict missing sensitive attribute labels. However, their reliance on the accuracy of the predicted pseudo labels resulted in suboptimal data utilization, particularly in extremely limited sensitive situations. Different from previous papers, explore group fairness in limited sensitive attribute situations and can efficiently utilize data with known sensitive attributes without predicting missing sensitive attributes.
3 Problem Definition
In user modeling, we denote U = {u:} and V [u as the user set and the item set, respectively. The interactions between users and items generate interaction records R. whose element is denoted triplet (u, v. where u U, e V, and r is the corresponding interaction decided by the corresponding task. As we have assumed that sensitive attributes are only available for a limited number of users, we split the user set U into two disjoint sets as U2 = {ur) and UN = {u, H so that the sensitive label is only accessible for each user in U1 instead of UN. Let S be the sensitive attribute that takes value in S, whose values for users in UL are available, i.e. {si|s E SY- Our goal is to learn user model f that generates fair representative characteristic 0 for each user u. Following previous studies [3. 21, 42], the fairness requirement for user modeling is that the generated outcome should not expose any sensitive information, which be depicted the result of zero mutual information between the user's generated characteristic A and sensitive attribute S, i.e. S) = 0. In this way, we have the following problem definition:
Problem 1. Given interaction records R, users U, items V and the sensitive label set {8i) for Ur. U, the goal is to learn user model f U - e that generates a fair representative characteristic E O for each user u E U, such that T(0,S) S) = where S is the corresponding sensitive attribute.
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FairLISA
4.1 Overview
FairLISA is an adversarial learning-based framework designed to promote fair model training by efficiently utilizing data with known and unknown sensitive attributes. The architecture is shown in Figure I It consists of two modules including a filter and dis- criminator D. Specifically, for a given user with the original representation 0 generated by any user models, we use the filter F trained on data with known sensitive attribute Uz, and data with unknown sensitive attribute UN to filter out the information of sensitive attributes. The filtered user modeling result of user is denoted as 0 F(0). To guide the filter F training, we use the idea of adversary learning train a discriminator D. The goal of the discriminator 'D to fail the filter F. which achieved by predicting the corresponding sensitive attribute from the filtered user rep- resentations 0. Through adversarial training between FF and D, we can get a filter to re- move the effect of sensitive attributes. Unlike traditional adversarial methods B 23 45 that only rely on UL, FairLISA can train filter
Lx = Stask
+
d2LL +
AaCN
D1
Filter F
θ
Discriminator
D2(0)
D
Dis)
Data with known sensitive attributes
LD
Data with unienown sensiiive attributes
The architecture of FairLISA. For given user with the original representation @ generated by any user models, Uy and UN to remove the sensitive information and get the filtered representation é. Then we use the discriminator D trained on U1 to predict the sensitive feature of ê. Through adversarial training and the better utilization of UN. we can get a better filter to remove sensitive information.
Figure I:
first use the filer FF trained on
with UL and UN, i.e., Á uu (0). Following the previous study (5), assume the distribution of users' filtered representations in Uz and UN be the same and use p(0) represent them. We will introduce how FairLISA achieves the fairness goal (i.e., I S) = 0) through Un, UN respectively and how to utilize these two types of data to form the final architecture. For simplicity of theoretical analysis, we assume there is one sensitive attribute (e.g., gender) and s (c.g.. female) is an attribute value of S.
4.2 Fairness with Known Sensitive Attributes
We will begin by explaining how FairLISA achieves (θ, with data with known sensitive attributes UL. i.c., i = Fuz (0). More specifically, we will describe how to establish connection between Ur. and I [ S). as well as how to leverage data with known sensitive attributes to minimize ((0, S) based this connection. In this regard, define (0) as the probability that the discrim- inator D recognizes the value of the sensitive attribute S based on Ô. H denotes the shannon entropy (24) According the following lemma, mutual information [Ū, S) can be expressed the optimization function shown below, when the optimal discriminator can achieve (7) = p(=10).
Lemma 4.1. Given the optimal discriminator D (0) and let p(0)s) be the distribution of users' filtered representations whose sensitive attribute value is s, then I(0. S) can be derived as,
I(0,5) = Σ ((s)) [Lg D (0) + H(S).
(1)
The detailed proof is provided in Appendix A.1. We have established a connection between Uz and T(,,S) .S) through Lemma A.1 Consequently, faimess can be achieved by minimizing Eq. (11) with fixed optimal discriminator D It important to note that estimating p(0)s) in Eq. (TI requires access to sensitive information. This implies that leveraging Un (data with unknown sensitive attributes) to enhance fairness becomes impossible. However, in real-world applications, users are often concerned about privacy and may be unwilling disclose their sensitive information. The limited availability of sensitive attributes means that only restricted amount of data with known

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sensitive attributes can participate in model training, leading to an insufficient training data problem for existing methods. In the following sections, we will introduce how alleviate this problem by effectively utilizing data with unknown sensitive attributes.
4.3 Fairness with Unknown Sensitive Attributes
An intuitive approach to leverage data with unknown sensitive attributes UN is to predict the missing sensitive labels based on Ur. However, the accuracy of these predicted pseudo-labels cannot be guaranteed, which results in suboptimal data utilization. Therefore, is crucial to explore new methods that fully harness the potential of Uy to enhance fairness. Given that the suboptimal data utilization stems from predicting missing attributes, natural idea is to directly employ UN without predicting missing attributes, which can avoid information loss caused by prediction and maximize the utilization of UN to greater extent. To achieve this objective, it necessary to directly establish relationship between I(0,5) 5) and UN. Drawing inspiration from Lemma A.1 and structural insights related to the KL divergence [II] we conclude that shannon entropy H can build this relationship.
Lemma 4.2. Given the optimal discriminator (0) and let p(0) be the distribution of users' filtered representation. we have,
I(0,5) S) = -Bipp(h) Σ (0) log (:( + H(S)
-H( H()).
Proof. According to the relationship between mutual information and JS divergence, the relationship between JS divergence and KL divergence [24], we have
JSp Σ p(s))LL(((s)))00)))
(3)
By calculating KL divergence. we get,
[p(s)K((((()llb /Σ ((s)p(((s)log log p(0]s) p(0)
dô.
(4)
Then we expand the formula and use bayes' theorem,
/ Σ p(s)p(0(s) log p(0]s) db p(0)
(5)
p()) Σ p(s|0) log (ogg(((s0@ddl H(S).
Noted that p(s|0) is exactly D", we replace it in the formula, and get the result,
/ p(0) Σ p(s log logp(s)0)dd + H(S)
(6)
--((D'(0) H(S).
Here give the key steps of proof, more details are provided in Appendix A.2. In Lemma A.2 the term p(0)s) () disappears, removing the obstacle that hindered the direct utilization of UN as discussed in Lemma A.I Thus, we have successfully established the relationship between (0, S) and UN through Lemma A.2 We also provide an intuitive illustration. By maximizing the entropy of the discriminator, we can ensure that the prediction probability of sensitive attributes becomes uniform. For example, in the case of gender prediction, both male and female probabilities are set to 0.5. This shows that the discriminator becomes unable to predict the sensitive features from the learned user embedding. Consequently, we can minimize our fairness criterion T directly by minimizing Eq. (15) using UN. This allows for the effective utilization UN to enhance fairness without the need to predict unknown sensitive attribute labels.
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4.4 Architecture
In this section, we now introduce how to combine Uz and UN together to form the final architecture (shown in Figure
order to obtain higher improvement of fairness, i.e., 0 FuL+Ux (0). By leveraging the insigtts from Lemma A.1 and Lemma A.2 we have established the connection between UL. UN, and the fairness goal I S). Thus, faimesss
be achieved through minimizing Eq. (IT) with Uz and minimizing Eq. (15) with UN with a fixed optimal discriminator D To achieve this goal, we first optimize the D to the best, then fix D to optimize F. FairLISA consists of a filter and a discriminator. We will introduce them respectively as well as the training algorithm.
Filter. We use the whole data to optimize the F. For data with known sensitive attributes, we The empirical estimate can be computed as:
optimize F by minimizing the right side of Eq. (IT).
L1 Σ
Σ
log Dz(())..
1Sighe,
where 0g is the filtered representation u, Here we omit (S) in Eq. 11 since it a constant.
For data with unknown sensitive attributes, optimize F by minimizing the right side of Eq. (55 The empirical estimate can computed as:
n
E Eo.(0.)og(((.0)))
(8)
1+1giShtn
where 0, is the filtered representation of uy Here we also omit (S) since it is constant.
Furthermore, in addition to the fairness requirement, characteristics for different user modeling tasks (e.g., recommender system, cognitive diagnosis). task-specific loss function to mine useful information of users. With the
need maintain users' useful potential
To achieve this, combination of Eq. (7) and Eq. (8), we get the following loss function:
min Lx X Ltask + X2L1 AahN,
where Ltask represents the loss of specific user modeling task, Å1 A2 and A hyperparameters. We study the influence of these hyperparameters in the experiment section (RQ3).
Discriminator.
To guide the filter F training, the goal of the discriminators is to predict the corresponding sensitive attribute from the filtered user representations Ó. Therefore, for the loss function DD we optimize the discriminator with data UL using the following cross-entropy:
min LD D
- Σ
log (O.),
(10)
8 ISichss
where 6 is the filtered representation of u, Here we omit (S)) in Eq. 11 since it is a constant..
Training Algorithm. Since the discriminator not be optimized the best directly, following the traditional adversary learning training methods [[2], we adopt mini-batch training in implementation. Specifically, 1) T mini-batch updates minimizing Le with the D fixed; 2) T mini-batch updates minimizing LD with the F fixed. Here T 10 in our implementation. The whole optimization algorithm for FairLISA is shown in Appendix B.
4.5 Relation to Existing Adversarial Methods
In this paper, we provide novel mutual information perspective to interpret the strategies for achieving faimess in existing adversarial methods, and unify the treatment of data with known and unknown sensitive attributes. As a result, FairLISA can easily generalize most existing fairness-aware adversarial methods with data with unknown sensitive attributes. For example, if we set xg in Eq. (9) to O, which means ignoring the data in UN. FairLISA degenerates ComFair [3), classical farrness-awe aware adversarial method. Moreover, our faimess mutual information objective is compatible with existing adversarial methods and can theoretically expand them to the limited sensitive attribute situation. For example, if we combine Eq. (8) from FairLISA with FairGo 45 a sfate-of-the-at fair model which considers graph structure, we could enhance its fairness performance. We will show the performance in experiments (denoted as FaiSG.+FaiLLIAA).

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Experiments
In this section, we first introduce the dataset and experimental setup. Then, we conduct extensive experiments to answer the following questions:
RQ1: Does FairLISA outperform the aawre baselines in limited sensitive situations?
RQ2: Can FairLISA achieve more robust fairness results on different missing ratios?
RQ3: How do the data with known and unknown sensitive attributes impact the results?
RQ4: How is the performance of FairLISA on several classical group fairness metrics?
The code is released at http://gthhbbooomm//zzg/9999/11
5.1 User Modeling Tasks
To explore fairness in user modeling with limited sensitive information, we choose two representative issues in real-world user modeling scenarios. The first is user capability modeling in areas such as education which is called cognitive diagnosis [40]. The user u, item in our general user modeling refer to student and exercise correspondingly. and interaction result r the score that the student got on exercise. The goal is to model the capability of students. To achieve this, we train models on student performance prediction task. We choose several representative models: IRT 300. MIRT (4) NCD [41] The second is user preference modeling in recommender system [39] The user u, item here refer to the customer and product respectively. The interaction result in r the user evaluation
of the product, such as rating behavior. By user rating prediction task, we can mine user preference.
We choose several representative models: PMF (34), NCF [15], LightGCN [6. Detailed basic user
model descriptions will be shown in Appendix C.I.
5.2 Dataset Description
We use two real-world datasets to verify the effectiveness and robustness of FairLISA. Specifically, we use PISA201 for cognitive diagnosis and MovieLens- IM for recommender system. Detailed descriptions can be found in Appendix C.2. Both of these datasets are publicly available and do not contain any personally identifiable information. our experiments, we split each dataset into training, validation, and testing sets, with ratio of 7:1:2. All baseline models were trained and evaluated using the same datasets.
5.3 Experimental Setup and Baselines
Evaluation. The evaluation of our methods can be divided into accuracy evaluation and fairness evaluation. For accuracy evaluation, we select different accuracy metrics for different tasks. For cogmitive diagnosis, following previous works from cognitive diagnosis [4] g we adopt different metrics from the perspectives of both regression (MAE, RMSE) and classification (AUC, ACC). For recommender system task, we adopt RMSE.
For fairness evaluation, our fairness goal refers to user modeling results do not leak any user's sensitive information. To measure the fairness performance, following the settings in fair user modeling such as 3 23 45), we train an attacker who has the same structure as the discriminator. Specifically, after training our framework, sensitive attributes as labels to train the attacker. If the attacker can distinguish sensitive features from the user representations, it indicates that the sensitive information is exposed by user modeling results. To train and evaluate attackers, we split the data into train (80%) and test sets (20%) and calculate the AUC score and apply their macro-average make the result insensitive imbalanced data. The smaller values of AUC denote better fairness performance with less sensitive information leakage, which be used as the unfairness metric the following experiments.
use the filtered user representations input and the corresponding
http://wwwww...c...oggggs/////// /databasse/
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Table 1:
The fairness and accuracy performance on recommender system task. AUC represents AUC scores of all attackers. The smaller values of AUC denote better faimesss performance with less sensitive information leakage (the fairer). G, A, O represent gender, age, and occupation. RMSE represents accuracy performance. The best fairness and accuracy results methods are highlighted in bold. The runner-up accuracy results are represented by underline.
PMF
NCF
LightGCN
AUC-G
AUC-A
AUC-O
RMSE
AUC-G
AUC-A
AUC-O
RMSE
AUC-G
AUC-A
AUC-O
RMSE
Origin
0.6862
0.7235
0.6656
0.8670
0.6915
0.7153
0.6625
0.8635
0.7112
0,7053
0.6939
0.8301
ComFair
0.5389
0.5554
0.5398
0.9027
0.5379
0.5374
0.8994
0,5498
0.5498
0.8704
FairGo
0.5592
0.5592
0.5371
0.8933
0.5351
0,5412
0.5319
0.8941
0.5457
0.5435
0,5408
0.8658
FairGNN
0.5288
0,5370
0.5212
0.9025
0.5294
0.5305
0.5256
0.9009
0.5289
0.5315
0.5225
0.8648
ISA
0.5193
0.5240
0.5210
0.8935
0.5281
0.5220
0.5232
0,8853
0.5273
0.5291
0.5214
0.8564
FairGrs Fairl ISA
0.5132
0,5270
0.5112
0.8927
0.5273
0.5204
0.5214
0.8867
0.5241
0.5260
0.5208
0.8628
Implementation detail. For task-specific loss Ltosk in Eq. use cross-entropy loss for cognitive diagnosis tasks and MSE loss for recommendation tasks. For all datasets and models, we set the learning rate 0.001 and the dropout rate 0.2. We select the best model based on its original performance on the valid set and use it to conduct the fairness test. We set adversarial coefficient A = 1, A2 2, A3 for cognitive diagnosis, and X 1, A2 20, λ 10 for recommendation tasks. For the adversarial architecture, the filter module is two-layer perceptrons and we use ReLU as the activation function. The discriminators and attackers are three-layer perceptrons with the activation function of LeakyReLUU. We set the dropout rate to 0.1 and the slope of the negative section for LeakyReLU 0.2 for them. We implement all models with Py PyTorch and conduct all experiments on four 2.0GHz Intel Xeon E5-2620 CPUs and Tesla K20m GPU.
Baseline approaches. To validate the effectiveness of FairLISA, we compare FairLISA with the following baselines. Origin (i.e., basic user models without fairness consideration), ComFair B]. FairGo 455. FairGNN (S. Further, to validate the generalizability of FairLISA, we expand FairGo to Fai (introduced in section 4.5) A detailed description will be shown in Appendix C.3.
5.4
Experimental Results
Overall results (RQ1). The fairness and accuracy results of the recommendation task are shown in T
The results of cognitive diagnosis are shown in Appendix C.4. We have several observations.
From the perspective of model fairness, the AUC of attackers on origin user models is significantly higher than 0.5, which means that the sensitive information is exposed by user modeling results even if such information is not explicitly used. Further, models which can simultaneously apply data with known and unknown sensitive information (i.e., FairGNN, FairLISA, FairGo+FairLISA. get better fairness performance. It shows that data with unknown sensitive attributes benefit fair user modeling. More importantly, we find our methods (i.e., FairLISA, FairGo+FairLISA) outperform others, suggesting the effectiveness of our work.
From the perspective of model accuracy, all fairess-immproving methods lose some accuracy compared with origin user models. The reason is that the fairness-aware methods are aiming at filtering out the information of certain sensitive features from user modeling results, which will to some extent reduce the information contained, thus decreasing accuracy [ 33 45). Among these fairness- methods, we could find FairLISA, FairGo+FairLISA have the best accuracy performance, and they could even maintain comparable accuracy with origin models in some cases.
Performance on differrntt ratios of missing sensitive attributes (RQ2). In this part, we study the impacts of sensitive attributes missing ratios and investigate the robustness of FairLISA in extremely limited sensitive situations. We choose the representative models which achieve the best performance result in RQ1 (i.e., NCD, LightGCN) and vary the ratio of missing sensitive attributes as (20%, 40%, 60%, 80%, 95%} in the training set. From the results in Figure 2 we have the following findings.
The sensitive attributes missing ratios have huge impact on the fairness results. As the missing ratios increase, the fairess--vwe aware methods are not effective enough. Especially, when the missing
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0.20
0.55
0334
0.30
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MiJit ratne suasng Depposde Seastioo Attiekk Geaks
Mi rite maaig Scaviie Aibhekc Gesks
Misazz rite tarnng Regoreeakky (lig Sondiio Aftilaac Age
Figure Fairness performance on different missing sensitive attributes ratios ( (the lower, the fairer).
ratio reaches 95%, several fairness-aware baselines converge to the Origin baseline. This obser- vation indicates that in the absence of extremely sensitive attributes, other baseline methods have essentially lost their effectiveness in achieving fairness and perform similarly the case where fairness was not considered at all. This highlights the point that fair user modeling in limited sensitive situations worth studying.
The models that can deal with unknown sensitive attributes, i.e., FairGNN, FairLISA, FAi perform better in most cases. It shows that making use of data with unknown sensitive attributes can alleviate the limited known sensitive information problem to some extent.
Our methods, i... FairLISA and FairGo+FairLISA, consistently achieve the best performance. As the missing ratio increases, the improvements of methods over FairGNN become more prominent. When the missing ratio reaches 95%, FairLISA can achieve an 8% improvement in faimess compared to FairGNN. This is due to FairGNN's reliance the accuracy of the estimator. When numerous sensitive attributes are present, the accuracy of the sensitive estimator be ensured. Nevertheless, FairGNN encounters notable constraints when access to sensitive information is limited. Our methods, based on information theory, can directly apply data with unknown sensitive attributes improve fairness and are not greatly affected by the missing ratios, demonstrating the robustness of our methods.
Influence of UL, UN (RQ3). FairLISA can directly leverage UL and UN to improve fair- ness. In this part, we investigate their influence by adjusting the hyperparameter X2 (U,,), X3 (Ux) as defined in Eq. (9) on two user models (i.e., NCD, LightGCN). Additional results are included in Appendix C.5. Specifically, we vary the values of X2, A3 on NCD as {0, 0.5, 1, 1.5] and the values of X2 λ on LightGCN as {0. 5, 10, 15). The results are shown Figure 5 (the lower, the fairer). We can find that A2 has greater impact than A3 indicating that UL has more significant role in promoting fairness. This is reasonable because sensitive labels in Uz can guide the filtering process. Moreover, as the values of hyperparameters increase, the results become fairer, suggesting that both UL and UN contribute fairness.
0.51
0.70
0.50
12:00
0.43
Sesttine Ancheiic Regin
Smuttine Arrtheic Deciuaton
Figure 3: Effects of X2, A3 on user models.
Relation to classical group fairness met- rics (RQ4). Previous experiments have demonstrated the effectiveness of FairLISA in terms of preventing the leakage of sensitive in- formation from representations. In this section, we present the results of our proposed model on two classical group fairness metrics, namely Demographic Parity [7] and Equal Opportunity 13 (the lower, the better). The specific formulas for these metrics and additional results are included Appendix C.6. In short, the results shown in Figure indicaae that our goal of removing the effect of sensitive attributes also benefits the classical group fairness metrics, and models achieve the best performance.
Orgin
012
0.30
Coeshalr
Danty
Expal Opportriity
RacON
Platoon FarLSA
Crnder Recemmender LighGGN
LightOCN
Figure 4: Performance of different group fairness metrics (the lower, the better).

---

6 Conclusion
In this paper, we focused on the fairness problem of user modeling and proposed a general framework FairLISA as solution in limited sensitive information situations. We first provide a novel theoretical perspective to unify data with known and unknown sensitive attributes with the fair mutual information objective. Building enables us to effectively leverage the entire user dataset for fair user modeling. We further showed that FairLISA could be seen generalized framework from traditional adversarial methods and take better advantage of data with unknown sensitive attributes. Experimental results on different user modeling tasks showed the effectiveness of FairLISA.
this unification, we propose an adversarial learning-bass -aaed framework that
7 Border Impact and Future Work
User modeling is crucial task in various applications and has significant impact on various aspects of our daily lives. In this paper, we discuss the social responsibilities of user modeling and consider situation where limited sensitive attributes are available. Our model could better help to design fairness-aware user modeling tasks and also protect users' privacy in the user modeling process. However, we notice that despite considering limited sensitive situation, there is still need to collect part of users' private information as model input, which may increase the burden on users as well as incurs possible privacy leakage in the data storage and transfer process. In the future, we will try to combine the privacy and fairness concerns so as to explore fairness and privacy aware user modeling.
Acknowledgments and Disclosure of Funding
This research was partially supported by grants from the National Key Research and Development Program of China (Grant No. 2021 YFF0901003) and the National Natural Science Foundation of China (Grants No. 62337001, U20A20229).
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Appendix
In the appendix, start from the proofs of Lemma 4.1 and Lemma 4.2 in Section A After that, introduce the whole optimization algorithm for FairLISA in Section Finally, the supplementary experimental introduction and results reported in Section C
A Proof
FairLISA is an adversarial learning-basee framework designed to promote fair model training by efficiently utilizing data with known and unknown sensitive attributes. consists of two modules: filter module and a discriminator module. Specifically, the filter module F is trained to filter out the information of sensitive attributes (e.g., gender) of We denote the filtered user modeling results of a user as: i F(0). The discriminator module D tries to predict the corresponding sensitive attribute from the filtered user representations. The fundamental concept behind Fair is to establish a connection between two types of data and the fairness objective (i.e., (0, S) 0) through Lemma 4.1 and Lemma 4.2. By doing so, it effectively utilizes data with both known and unknown sensitive attributes to facilitate fair model training. In the following sections, will provide detailed explanation Lemma 4.1 and Lemma 4.2, respectively.
A.1 Lemma 4.1
In this proof, we explain how FairLISA builds the relationship between fairness goal T() = 0 with user data with known sensitive attributes Ur. through Lemma A. We define D.(0) as the probability that the discriminator 'D recognizes the value of the sensitive attribute S as 8 according to 0, p()) be the distribution of users' filtered representations.
Lemma A.1. Given the optimal discriminator (0) and let p(0ss) be the distribution of users' filtered representations whose sensitive attribute 8, then T(0, 5) can be derived as
1(0,S) = Σ p(( D' (0) + H(S).
(11)
Proof. We use Lo represents the loss of discriminator D, first prove that when Lp Dog (0)) the discriminator achieve the optimal.
Consider the constraints of the discriminator E, (0) 1, use Lagrange multipliers:
LD = Σ E(())BBapriie log(D. + Σ \ D.(00
(12)
Differentiate LD. with respect to (0) and Aa and make the corresponding result O, get:
p(s) p(0)s) + A = Vs,
ED.00) -1 = 0, vo,
(13)
D
therefore, we calculate that (0) p(s(0). Then substitute it into the right side of the formula and get,
Σ p(s E B (0) H(S)
Σ p(s) 1og Σ p(s) log
Σ p(s)p(0ls) log p(0ls) db
(14)
p(0)
Σ L(s))L(p(()))))
-S T(0.5)
14

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Here, the second last equality is due to the definition of the Jensen-Shannon divergence, and the last equality due its equivalence the mutual information 24 This completes the proof.
We have established a connection between Uz and (0, through Lemma A.
Consequently,
fairness be achieved by minimizing Eq. (TT) with fixed optimal discriminator D
A.2 Lemma 4.2
In this proof, we explain how FairLISA builds the relationship between fairness goal ((0,5) = 00 with user data with known sensitive attributes Un through Lemma A..
Lemma A.2. Given the optimal discriminator (0) and let p(0) be the distribution of users' filtered representation. have,
1) = =B Σ (2) log (2 HH()) H(S)
(15)
-H( H(S).
Proof. According to the relationship between mutual information and JS divergence, the relationship between JS divergence and KL divergence 24], we have
T( 5) = JSp(s) (p(0)s)) = Σ p(s) p(s))T(((())))p0(0))
(16)
By calculating KL divergence, we get,
/Σ p(s)p(0|s) log p(0|s) p()) d0.
(17)
Then we expand the formula and use bayes' theorem,
f Σ ((s))((()s) log p(0|s) db
p(0) = / E p(s)p(0(s) log p(s)p(0(s) db + H())
p())
(18)
/ p()) Σ p(s)p((1s) ((s))(01s) di H())
log
p(0) p(0)
/ p()) Σ p(s|0) logp(s H(()).
Noted that p(s(0) is exactly D", we replace it in the formula, and get the result,
/ p(0) Σ log H(S)
/ (()) Σ (2 log (0) H(S)
(19)
E Eppp(0)
Σ D (0) log (
- (0) H(S).
This completes the proof.
We have successfully established the relationship between T(O, S) and Un through Lemma A.2 Consequently, we can minimize our fairness criterion T(0, S) directly by minimizing Eq. 115) using UN without the need to predict unknown sensitive attribute labels.
15

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Algorithm 1: The FairLISA framework
Input: Training set U: item set V; interaction records R; Filter F; Discriminator D Training epochs M; Discriminator training steps T; hyperparameters A1 X2, A
Initialize: User modeling results 0, V0 U
for epoch 6- to M do
for u E U do
i F(8);
Optimize FF through Ly in (q) with D fixed; / to do
for Optimize through LD in (q.( with 0, F fixed;
end
end end
B Training Algorithm for FairLISA
C Supplementary Experimental Results
C.1 User Modeling Tasks
To verify the effectiveness of model, choose two representative issues in real-world user modeling scenarios. The first is user capability modeling in areas such as education which is called We choose several representative models:
cognitive diagnosis 40
IRT [30] is cognitive diagnosis method that models the cognitive processes from students' exercise records with logistic-like function.
MIRT f is variant of the basic IRT model, which extends the unidimensional latent traits of students and exercises in IRT to multidimensional vectors.
NCD 41 is a deep neural cognitive diagnosis framework that models the complex interaction from students' exercising records with multilayer perceptron (MLP).
The second is user preference modeling in recommender system tive models:
39
We choose several representa-
PMF 34 the Probabilistic Matrix Factorization algorithm by adding Gaussian prior distribution of user and item latent factors into the classical matrix factorization algorithm.
NCF [15 is a state-of-the-art collaborative filtering model based on deep neural networks.
LightGCN [6]
state-of-the-arr GCN-based recommender system model which learns user embeddings by linearly propagating them on the user-item interaction graph.
C.2
Dataset Description
PISA201 is released by Program for International Student Assessment (PISA) which collects students' information from exercises, and the records between students and exercises are 1 11,488,234, We select region, gender, family education, and family economic sensitive attributes. For region, users are divided into two groups according to whether users' region belongs to Organisation for Economic Co-operation and Development (OECD) countries. According whether the highest education degree of parents is above primary level, we divide users into two groups Similarly we do so according to whether there are houses belonging users. More details the PISA dataset can be found (52)
hundred regions countries. It contains 403,609 students, 183
ens-el 1 is benchmark dataset for recommender systems. contains million rating records for about 4,000 movies from 6,040 users. We select gender, age, and occupation
16

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Table 2: The fairness performance of baselines cognitive diagnosis task (AUC scores of all attackers). R, A, E, C represent region, age, family education, family economic. The smaller values of AUC denote better fairness performance with less sensitive information leakage (the fairer). The best results are highlighted in bold.
IRT
MIRT
NCD
AUC-R
AUC-A
AUC-E
AUC-C
AUC-R
AUC-A
AUC-E
AUC-C
AUC-R
AUC-A
AUC-E
AUC-C
Origin
0.5864
0.5191
0.5930
0.5565
0.6271
0.5351
0.6329
0.5831
0.6307
0.5344
0.6327
0.5808
ComFair
0.5661
0.5178
0,5858
0.5515
0.5727
0.5226
0.5806
0.5618
0.5786
0.5300
0.5795
0.5609
FairGo
0.5699
0.5179
0.5828
0.5489
0.5965
0.5624
0.5724
0.5220
0.5950
0.5597
FairGNN
0,5166
0.5165
0.5452
0.5284
0.5680
0.5181
0.5952
0.5562
0.5761
0.5209
0.5944
0.5591
Fai
0.5412
0.5163
0.5490
0.5279
0.5540
0.5154
0.5727
0.5429
0.5540
0.5154
0.5727
0.5429
FaiIGo+FanLLSA
0.5355
0,5152
0,5419
0,5279
0.5510
0.5154
0.5720
0.5422
0.5622
0.5190
0,5704
0.5513
Table 3: The accuracy performance baselines on cognitive diagnosis task. Bold represents the best
results, and underline represents runner-up results.
IRT
MIRT
NCD
ACC
AUC
MAE
MSE
ACC
AUC
MAA
MSE
ACC
AUC
MAE
MSE
Origin
0.6932
0.7475
0,3932
0.2042
0.7152
0.7868
0.3529
0.1903
0.7486
0.8111
0.3555
0.1704
ComFair
0.6834
0.7380
0.3953
0.2095
0.7141
0.7864
0.3607
0.1887
0.7253
0.8000
0.3605
0.1816
FairGo
0.6853
0.7422
0.3996
0,2072
0.7147
0.7804
0.3618
0.1897
0.7299
0.8019
0.3597
0.1799
FairGNN
0.6853
0,7423
0.3972
0,2084
0.7110
0.7857
0.3616
0.1893
0,7301
0.8025
0.3584
0,1800
PairLISA
0.6923
0,7469
0,3942
0,2044
0.7148
0.7867
0.3565
0.1896
0,7300
0.8039
0.3572
0. 0.1696
FairGo+FairLISA
0.6905
0,7411
0.3927
0,2045
0.7140
0.7864
0.3590
0.18
0.7313
0.8041
0,3573
0.1793
sensitive features, where gender is a binary feature, occupation is 21-class feature, and users are assigned to 7 groups based on age.
C.3
Fairness-aware Baselines
we compare FairLISA with the following baselines:
Origin: basic user models without faimeess consideration.
ComFair 3 is classical fairness-aware adversarial method in fully sensitive attributes situations.
FairGo 45 is the state-of-the-art method improve faimess in fully sensitive attribute situations from a graph perspective.
FairGNN S is the state-of-the-ar fairness-aware method in limited sensitive situations relying on a sensitive attribute predictor.
C.4 Result of Cognitive Diagnosis
The fairness and accuracy results of cognitive diagnosis task are shown in Table Table 3 several observations from the out outcomes.
We have
From the perspective of model fairness (i.e., Table 2). the AUC of attackers on origin user modeling models are significantly higher than 0.5, which means that the sensitive information is exposed by user modeling results even such information aware methods can improve fairness. Among these methods, models which can simultancously deal with the known and unknown sensitive attributes information (i.e.. FairGNN, FairLISA. FairGo+FairLIAA) get better fairness performance. It shows that data with unknown sensitive attributes also benefit fair user modeling. More importantly, we find our methods (i.e., FairLISA, FairGo+FairLISA outperform others, suggesting the effectiveness of our work.
not explicitly used. Further, all fairness-
From the perspective of model accuracy (i.e., Table 3), all fairness-impron accuracy compared with origin user models. This is the inevitable result of the trade-off between accuracy and faimess B 33 45). The reason is that the faimess-awan aware methods are aiming at filtering out the information of certain sensitive features from user modeling results, which will to some extent reduce the information contained, thus decreasing accuracy. Among these fairness- aware methods, we could find FairLISA, FairGo+Faiir have the best accuracy performance, and they could even maintain comparable accuracy with origin models in some cases.
methods lose some
17

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0.70
0/0-
AUTC
0,00
0.55
0.4
0.00
040-0
Ongaitie Drgronte NET (lggoe)
Degatte Dogroea: NED (Gamddr)
Cognttss Riggnde NCD (idacabor)
Cogsitox Ragsodi NCD (Bommmo
Figure 5: Effects of different hyperparameters on NCD.
060
0.00
AUC
0.30
030
0.4
030
Recommendder Symar:: LightELN Laandery
Koomerreder Synrem wageeennnnaggnnnn LightLacN CAge
Recommande Sysker: LigKGEN (UKE)
Figure Effects of different hyperparameters on LightGCN.
From the perspective of balancing the model accuracy and fairesss. Our methods achieve both better accuracy performance and fairness promotion in all cases. Hences, we argue that our framework achieves better performance in balancing the model accuracy and fairnsss, which also suggests that making full use of data with unknown sensitive attributes can alleviate the problem of fairness- accuurccy trade-off some extent.
C.5 Influence of UL, UN (RQ3)
FairLISA unifies the treatment of data with known sensitive attributes Uz and data with unknown sensitive attributes UN from mutual information perspective, thus Uz. UN can be directly leveraged to improve fairness. In this part, we investigate their influence on fair user modeling results by
adjusting the hyperparameter models which achieve the best performance results in RQ1 (i.e., NCD, LightGCN). More specifically, we vary the values of X2,
(Uz), As (Ux) As for the user models,
choose the representative
NCD as (0, 0.5, 1.5) and the values of A2, 5. 10, 15), J. The results are shown in Figure 5 (the lower, the fairer). On the one hand, we find A2 always has huge impact than Ag. which means U, makes greater contribution fairness than UN- This is reasonable because of the sensitive attribute labels in UL being as the guidance to filter the effect of sensitive attributes. On the other hand, as the values get larger, both the results get fairer. which indicates that both Un and UN can contribute to fairness. This observation also demonstrates that making use of U can improve the fairness of the user modeling result.
LightGCN {0,
C.6 Relation to classical group fairness metrics (RQ4)
Previous experiments have shown the effectiveness our framework in terms avoiding leaking sensitive information from user modeling results. In this part, show the results of proposed
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Figure 7: Performance of group fairness metrics NCD and LightGCN (the lower, the better).
18

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Origa
Denegrapies Perte
Oppoitusis
Opportuity
PARONN
FaaLISA
Cagattis: Dagnosis: MIRT
Resimmnnd System: NCF
Orgailio: Dagnsis: MIRT
Rasemmnne System: NCF
Figure 8: Performance of group fairness metrics on MIRT, NCF (the lower, the better).
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Figure 9: Performance of group fairness metrics IRT, PMF (the lower, the better).
model classical fairness metries. We choose group fairness metrics (i.e., Demographic Parity 77. Equal Opportunity (13) to evaluate the results. Demographic Parity ( is widely used to measure the predicted rating discrepancy for binary valued sensitive attributes [45]. Take binary attribute (i.e..
gender) for example, the detailed formula is as DP = + > (fEemmte(fuee
Eugfemate [fwolll For attributes with multiple values, we borrow the idea of demographic parity and divide users into different groups based on their sensitive values. Then we take the standard deviation of predicted ratings of each user group to measure demographic parity. Equal Opportunity [13] is another classical fairness definition, advances demographic parity fairness by considering the parity of prediction accuracy of each group. For binary attribute, take gender for example, the detailed formulas are as EO Σn |[Euemate - Two] Eve femaielf... - Twolll. For attributes with multiple values, it has the same way demographic parity. The results of these two group faimesss metrics on the user models are shown in Figure / Figure Figure 9 We can find both user modeling tasks are unfair on these two classical metrics, which also indicates the necessity to explore fairness in user modeling. Then, fairness-aue awrre methods aiming to remove the effect of sensitive attributes can improve the fairness of the two metrics. It shows that the goal of removing the effect of sensitive attributes also benefits the classical group fairness metrics. Among all these methods, we could find that the methods (FaiGNNN, FairGo+FairLIA, FairLISA) can always achieve better results, which shows that necessary to explore fairness in limited situations. Meanwhile, we find FairLISA A can achieve the best performances, suggesting the effectiveness of our methods.
19


Reviews:
[{"review_id": "AsARnc31nr", "reviewer": "Reviewer_KpgW", "summary": "The paper proposed an algorithm to train fair user modeling (e.g recommender systems) when given limited sensitive attributes. The idea is to factorize out the effect of sensitive attributes from the model's fair training objectives, and isolate its impact in training.", "questions": "1. The evaluation focuses on privacy, which is how accurately an attacker can predict sensitive attributes from the user representations. But this evaluation would assume attackers can access user representation, which means the attacker is the model trainer, e.g. recommender system employer. Is it the case? If so, how can the attacker obtain the ground-truth sensitive attributes as the training labels of the attack model?", "limitations": "See Weaknesses.", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Studied an important problem in practical fair recommender system", "weaknesses": "1. The proposed algorithm only applies to generative-based user modeling, which is limited. Many user modeling methods are prediction-based.\n2. The high-level idea of decomposing the mutual information between prediction/embedding and sensitive attribute into sensitive-attribute-related term and non-sensitive-attribute-related term is common in the fairness literature. It seems limited novelty in the method.\n3. It seems the method still requires some samples with sensitive attributes labeled (the minimum in experiments is 20%). It would be good to clarify how practitioners can obtain those sensitive attributes. Because if the concern is about privacy, then getting 20% samples with sensitive attributes is as hard as getting 100%. In the literature, usually, the assumption is having some aggregated form of sensitive attributes rather than sample-level.\n4. A simple baseline is to train a classifier on samples with sensitive attributes and use it to label other data. This should be tested in experiments.\n5. Many grammatical errors and typos.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ">**Q1. The proposed algorithm only applies to generative-based user modeling, which is limited. Many user modeling methods are prediction-based.**\n\nA1. Sorry for the confusion! I quite agree that currently many user modeling methods are prediction-based.  In fact,  I'd like to clarify that the user models discussed in this paper are prediction-based models that do not have generative capabilities [5][6]. Our FairLISA is specifically used to ensure fairness guarantees for these prediction-based models, which borrows the idea of GAN to remove the effect of sensitive attributes, as shown in Section 4 of the paper. In the revised version,  we would reorganize this part to make it clearer. In the future, we intend to expand our work to generative-based user modeling, such as diffusion-based user models [7] and LLM-based user models [8].\n\n>**Q2. The high-level idea of decomposing the mutual information between prediction/embedding and sensitive attribute into sensitive-attribute-related term and non-sensitive-attribute-related term is common. It seems limited novelty in the method.**\n\nA2. In this paper, we study fairness in situations with limited sensitive attributes. Our core contribution lies in efficiently leveraging data with both known and unknown sensitive attributes to facilitate fair model training. To achieve this, we design a theoretical-driven model.  Different from the conventional mutual information-based framework, our framework stands out in two significant aspects: (1) We employ distinct approaches for handling two types of data. (2) We provide theoretical guarantees for both types of data. Moreover, the experimental results further validate the SOTA performance achieved by our approach. Thanks for your question, we would reorganize our paper to make it clearer.\n>**Q3. It would be good to clarify how practitioners can obtain those sensitive attributes. Because if the concern is about privacy, then getting 20% samples with sensitive attributes is as hard as getting 100%. In the literature, usually, the assumption is having some aggregated form of sensitive attributes rather than sample-level.**\n\nA3. I sincerely appreciate your suggestion. In fact, the fairness setting with certain sample-level sensitive attributes available has been proposed in the literature, such as [1][2][3].  The limited sensitive attribute labels can be obtained through various methods. Firstly, on open platforms, certain users may publicly share their profiles, like the example of 14% of teen users who expose their complete profiles on Meta [4]. Secondly, some researchers rely on human annotators to label limited sensitive attributes [3]. We will incorporate your suggestions in the revision of our paper. Moreover, we will explore methods to achieve fairness in sensitive properties using aggregate forms.\n\n>**Q4. A simple baseline is to train a classifier on samples with sensitive attributes and use it to label other data. This should be tested in experiments.**\n\nA4. We have already compared this baseline in our paper, called FairGNN. The core idea behind FairGNN is to train an estimator to predict missing sensitive labels using data with known sensitive attributes (as described in the Introduction, Section 6.1, and Appendix C.1). Furthermore, we discussed FairGNN in detail in Section 5. The experimental results demonstrate the effectiveness of our method when compared to the baseline. We will reorganize our paper in the reversion to remove any lingering doubts or confusion.\n\n>**Q5. Many grammatical errors and typos.**\n\nA5. Thank you for pointing out grammatical errors and typos. We will revise our paper to fix these errors.\n\n>**Q6. How can the attacker obtain the ground-truth sensitive attributes as the training labels of the attack model in the evaluation?**\n\nA6. Sorry for the confusion. The attacker is indeed a recommender system employer. Nevertheless, during the testing phase, we are assuming that the labels for sensitive attributes are obtainable. These labels serve solely as a ground truth to assess the attacker's ability to predict sensitive attributes, which in turn allows us to access the fairness of our model. Note that during the training phase, only a subset of sensitive labels are accessible, aligning with our real-world scenario. Such a setting is commonly adopted in situations involving limited sensitive attributes [1][2][3]. In the future, we will make the necessary revisions to elucidate these aspects in our paper and address your concerns.\n\n[1]Zhang F, Kuang K, Chen L, et al. Fairness-aware contrastive learning with partially annotated sensitive attributes[C]//The Eleventh International Conference on Learning Representations. 2022.\n\n[2]Kırnap Ö, Diaz F, Biega A, et al. Estimation of fair ranking metrics with incomplete judgments[C]//Proceedings of the Web Conference 2021. 2021: 1065-1075.\n\n[3]Enyan Dai and Suhang Wang. Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021.\n\n[4]Mary Madden, Amanda Lenhart, Sandra Cortesi, Urs Gasser, Maeve Duggan, Aaron Smith,\n486 and Meredith Beaton. Teens, social media, and privacy. Pew Research Center, 2013.\n\n[5]Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural\ncollaborative filtering. In Proceedings of the 26th international conference on world wide web, 2017.\n\n[6]Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, and Shijin\nWang. Neural cognitive diagnosis for intelligent education systems. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020.\n\n[7]Wang W, Xu Y, Feng F, et al. Diffusion Recommender Model[J]. arXiv preprint arXiv:2304.04971, 2023.\n\n[8]Wu L, Zheng Z, Qiu Z, et al. A Survey on Large Language Models for Recommendation[J]. arXiv preprint arXiv:2305.19860, 2023."}, {"review_id": "7IQG8Ct5fP", "reviewer": "Reviewer_teqm", "summary": "This paper proposes a novel adversarial learning method for fairness with limited demographics.", "questions": "See Weaknesses.", "limitations": "This paper seems to not discuss the limitations.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Pros:\n1. This paper focus on an important and practical problem. Fairness with limited demographics is a practical and important problem.\n2. This paper provides a theoretical-driven perspective on fairness with limited demographics.\n3. This paper proposed a novel adversarial learning method for fairness with limited demographics.\n4. Solid experiments are conducted to demonstrate the effectiveness of the proposed method.\n", "weaknesses": "Cons:\n1. The datasets used in this paper seem not very common. So it is a little bit hard to evaluate the effectiveness of the proposed method. It is suggested that authors also conduct experiments on commonly used fairness datasets such as ADULT and COMPAS.\n2. Baselines seem insufficient. It is suggested that the authors may consider adding more in-processing methods as baselines.\n3. This paper only empirically study the effectiveness of the proposed method. It is unknown whether or not the proposed method is theoretically better than the previous estimation-based method [1]. It is suggested the authors could add some theoretical analysis to demonstrate that the proposed method is better than the previous estimation method.\n4. The authors are encouraged to provide code for reproduction.\n5. Besides [1], the authors may consider citing some related references on fairness with limited exact demographics. Reference [2] studies the problem of fairness with limited clean sensitive attributes and mostly private sensitive attributes. Reference [3] studies the problem of fairness with active sensitive attribute annotation. \n\n[1] Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. https://arxiv.org/abs/2009.01454\n\n[2] When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes https://arxiv.org/abs/2207.08336\n \n[3] Mitigating Algorithmic Bias with Limited Annotations https://arxiv.org/abs/2207.10018\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ">**Q1. The datasets used in this paper seem not very common. So it is a little bit hard to evaluate the effectiveness of the proposed method. It is suggested that authors also conduct experiments on commonly used fairness datasets such as ADULT and COMPAS.**\n\nA1:We greatly appreciate your suggestion. In this paper, we study the fairness in **user modeling**, where the fundamental data format is (user, item, relation). This format is distinct from the ADULT and COMPAS table data formats, rendering these widely used datasets unsuitable for our purposes. Instead, we have chosen to utilize the classical fairness datasets commonly employed in user modeling studies [1][2], such as MovieLens-1M.\n\n>**Q2. Baselines seem insufficient. It is suggested that the authors may consider adding more in-processing methods as baselines.**\n\nA2: Thanks for your valuable suggestion. We have incorporated a new in-processing method called \"Reg[3]\" as our baseline. The core idea behind this baseline is to add the fairness objective as regularization. To validate our approach, we conducted additional experiments on the MovieLens-1M dataset, utilizing two user models (PMF and NCF). The results（i.e.,Table 1, Table 2） demonstrate that our methods (FairLISA and FairGo+FairLISA) outperform other techniques, affirming the efficacy of our research.\n\nTable 1: The fairness and accuracy performance on PMF. The AUC represents AUC\nscores of all attackers. The smaller values of AUC denote better fairness performance with less\nsensitive information leakage (the fairer). GEN, AGE, OCC represent gender, age, and occupation. RMSE\nrepresents accuracy performance. \n|   Baseline   | GEN AUC | AGE AUC | OCC AUC | RMSE |\n| --------------- | ---------- | ------- | ------- | ------- |\n| Reg | 0.6483 | 0.7368 | 0.6631 | 0.8945 |    \n|FairLISA | 0.5174 | **0.5276** | 0.5110 | 0.8912 |\n| FairGo+FairLISA | **0.5147** | 0.5316 | **0.5103** | **0.8812** |\n\nTable 2: The fairness and accuracy performance on NCF. The detail of evaluation metrics is the same as Table 1.\n\n|      Baseline   | GENDER AUC | AGE AUC | OCC AUC | RMSE|\n| --------------- | ---------- | ------- | ------- |------- |\n| Reg |    0.6814 | 0.5722 | 0.5886 | 0.8944 |\n|FairLISA | 0.5301 | 0.5218 | 0.5219 | **0.8831** |\n| FairGo+FairLISA | **0.5217** | **0.5205** | **0.5200** | 0.8892 | \n\n>**Q3. This paper only empirically studies the effectiveness of the proposed method. It is unknown whether or not the proposed method is theoretically better than the previous estimation-based method. It is suggested the authors could add some theoretical analysis to demonstrate that the proposed method is better than the previous estimation method.**\n\nA3: Thank you for your insightful suggestion. We acknowledge that conducting a theoretical analysis to establish the superiority of the proposed method is undeniably crucial. Nonetheless, it's imperative to recognize that addressing this challenge presents a significant undertaking. As an alternative avenue for evaluation,  we chose to substantiate our superiority through experiential insights. We will regard the theoretical substantiation of FairLISA as future work. Thanks again for your valuable suggestion.\n\n>**Q4. The authors are encouraged to provide code for reproduction.**\n\nA4: We greatly appreciate your suggestions. We strongly support open-source principles and will make the code public if our paper is fortunate enough to be accepted.\n\n>**Q5. Related references.**\n\nA5: Thanks for providing the references. These works are indeed pertinent to our research. We will incorporate these relevant references into the revision.\n\n[1] Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. Towards personalized fairness based on causal notion. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021.\n\n[2] Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, and Meng Wang. Learning fair\nrepresentations for recommendation: A graph-based perspective. In Proceedings of the Web Conference 2021, 2021\n\n[3]Yao S, Huang B. Beyond parity: Fairness objectives for collaborative filtering[J]. Advances in neural information processing systems, 2017, 30."}, {"review_id": "3xtTXcBEQE", "reviewer": "Reviewer_Gbnv", "summary": "This paper propose FairLISA that learns fair user modeling using limited sensitive attribute information. Specifically, for users with known sensitive attribute information, FairLISA maximizes the cross-entropy of predicting the sensitive attribute using user representations; for users with unknown sensitive attribute, FairLISA maximizes the entropy of predicting sensitive attribute using user representations. Experiments on benchmark datasets demonstrate the efficacy of the proposed model.\n", "questions": "Please see limitations.\n", "limitations": "L1. The authors may better illustrate why statistical parity is important in recommender system. To my understanding, many recommendation tasks is related to sensitive attribute as well. For example, a recommender system don't want to recommend feminine care items to male users.\n\nL2. Using user modeling as a motivating example is ok to me, but I don't understand why the authors position the paper to fair user modeling specifically. How does the proposed method connect to user modeling? What is the uniqueness of user modeling (other than limited sensitive attribute) in terms of fair user embedding learning? How does <user, item, relation> triplet useful here?\n\nL3. The theoretical analysis is based on the fact that discriminator is Bayesian optimal, which is often impossible. Thus, the practicability of the theoretical analysis needs more justification. \n\nL4. What if the discriminator is not optimal? Does the theoretical analysis still hold in this case?\n\nL5. Some intuition about the theoretical analysis would strengthen the intuition. For example, how does optimizing the entropy of the discriminator's output help with removing sensitive information. My guess is that it tries to make the prediction probability of sensitive attribute to be uniform. But the authors may clarify it better.\n\nL6. In Figure 2, why do all methods other than FairLISA converge to the same point? And it seems FairLISA converge to the same point when the ratio increases to 95% as well. Some discussion is needed.\n\nL7. FairGNN is developed in the setting of binary sensitive attribute and binary classification, where the covariance regularizer minimize the pseudo-sensitive attribute and the output logit scores. How do the authors extend it to non-binary sensitive attribute settings?", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1. Studying limited sensitive attribute scenario is practical and important.\n\nS2. FairLISA is supported by the theoretical analysis.\n\nS3. FairLISA's performance is good based on the empirical evaluation.\n", "weaknesses": "Please see limitations.\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ">**Q1. The authors may better illustrate why statistical parity is important in recommender system. To my understanding, many recommendation tasks is related to sensitive attribute as well. For example, a recommender system don't want to recommend feminine care items to male users. (L1)**\n\nA1. Sorry for the confusion. Recommender systems have a wide range of applications. In specific recommendation contexts, such as suggesting feminine care items, considering statistical parity may not be relevant since the preferences are inherently linked to gender. However, in other critical scenarios like career recommendations, it becomes essential to incorporate statistical parity. By doing so, we can prevent discriminatory outcomes and ensure fairness for all users, regardless of their sensitive attributes, this goal has been widely adopted the previous works, such as [1][2]. We will reorganize our paper to address your concerns.\n\n>**Q2.  Using user modeling as a motivating example is ok to me, but I don't understand why the authors position the paper to fair user modeling specifically. How does the proposed method connect to user modeling? What is the uniqueness of user modeling (other than limited sensitive attribute) in terms of fair user embedding learning? How does <user, item, relation> triplet useful here? (L2)**\n\nA2. The core of FairLISA is based on the widely adopted framework of fair adversarial learning. In other words, FairLISA can be applied to any domain where fairness adversarial learning is applicable. It is not restricted to specific data formats, such as <user, item, relation>. In this paper, we concentrate on the user modeling domain, as it plays a crucial role in decision-making. Moving forward, we intend to extend the application of FairLISA to a broader range of data formats, exploring its potential in various domains.\n\n>**Q3. Theoretical analysis. (L3,L4)**\n\nA3. The theoretical analysis demands an optimal discriminator assumption. However, I should mention that an optimal discriminator is a commonly ideal assumption in the limited sensitive research domain, such as [4]. Meanwhile, even when the sensitive attributes are not missing, achieving an optimal discriminator is also an ideal scenario. To evaluate the impact of the optimal discriminator assumption on our model, we conducted empirical experiments (RQ2). Specifically, we varied the ratio of missing sensitive attributes in the training set as {20%, 40%, 60%, 80%, 95%}. As the missing ratios increased, it became more challenging for the discriminator to reach an optimal state. By comparing our model's performance with baseline results, we could assess the impact. The experimental results demonstrated that our model achieved SOTA performance, even under the same missing attribute ratios. This indicates that the optimal discriminator assumption has minimal influence on our model compared to the baselines. Thanks for your suggestion and question, we will add more relevant discussions in the revision.\n\n>**Q4. Strengthen the intuition of FairLISA. (L5)**\n\nA4. Thanks for your suggestion. We will illustrate the intuition from the adversary learning perspective.\nBasic fair adversary learning comprises two modules:\nA discriminator module that predicts the sensitive features from the learned user embedding.\nA filter module that aims to fail the discriminator's performance.\nBy maximizing the entropy of the discriminator, we can ensure that the prediction probability of sensitive attributes becomes uniform. For example, in the case of gender prediction, both male and female probabilities are set to 0.5. This shows that the discriminator becomes unable to predict the sensitive features from the learned user embedding. Consequently, our goal of achieving fairness is accomplished. We will provide further clarification in the revised version.\n\n\n>**Q5. More discussion about experiments. (L6)**\n\nA5. Thank you for your suggestion. We observe that all methods, except FairLISA, converge to the **Origin** baseline in the missing ratio 95%. **Origin** refers to the model without fairness considerations. This observation indicates that in the absence of extremely sensitive attributes,  other baseline methods have essentially lost their effectiveness in achieving fairness and perform similarly to the case where fairness was not considered at all. In contrast, FairLISA demonstrates a more robust performance, showcasing its effectiveness in achieving fairness. We will provide a more comprehensive discussion on this in the revision.\n\n>**Q6. The non-binary setting expansion of FairGNN. (L7)** \n\nA6. Sorry for missing the detail information. \nThe covariance regularizer in FairGNN is  \n$$L_R=cov(\\hat{s},\\hat{y})$$\nwhile $\\hat{s}$ represents the predicted sensitive attribute and $\\hat{y}$ represents predicted label.\n\nIn the non-binary setting, $\\hat{s}$\ns a multi-dimensional vector. To implement the regularization, we first calculate the covariance matrix and then add the frobenius norm of the covariance matrix to the loss function as the regularization term. This technique has been widely adopted in various studies, such as [3]. We will provide a more comprehensive explanation of this setting in the revised version.\n\n[1] Le Wu, Lei Chen, et al. Learning fair\nrepresentations for recommendation: A graph-based perspective. In Proceedings of the Web Conference 2021, 2021\n\n[2] Yunqi Li, Hanxiong Chen, et al.Towards personalized fairness based on causal notion. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021.\n\n[3] Pourahmadi M. Covariance estimation: The GLM and regularization perspectives[J]. 2011.\n\n[4] Enyan Dai and Suhang Wang. Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021."}, {"review_id": "xNEilvcYjg", "reviewer": "Reviewer_xseN", "summary": "The authors investigate the problem of fair user modeling in a setting with limited sensitive attributes. Due to the lack of such attribute information, they propose a general framework called FairLISA, which efficiently applies unlabeled data to facilitate fair model training. Compared to previous works, FairLISA can directly leverage unlabeled data without the need for predicting missing attributes, thereby reducing information loss caused by predictions. The experiments demonstrate the effectiveness of the proposed model.", "questions": "Can fairness be extended to settings without sensitive attributes? \n\nCan the techniques used in settings without sensitive attributes [1][2] be integrated with FairLISA to attain improved outcomes in limited situations?\n\n[1]Tianxiang Zhao, Enyan Dai, Kai Shu, and Suhang Wang. Towards fair classifiers without sensitive attributes: Exploring biases in related features. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 2022\n\n[2] Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning, 2018.", "limitations": "As mentioned in the Border Impact section, FairLISA is effective only in limited scenarios. It is recommended to further explore the potential of FairLISA in completely unsupervised settings.", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The authors tackle a socially valuable problem of fair user modeling in a setting with limited sensitive attributes, which holds significant implications across various applications, such as recommendation systems and cognitive diagnostics. \n\nThe paper provides a thorough and insightful summary of related works on fairness without sensitive attributes and fairness in limited sensitive attribute situations. Building upon existing research, this paper effectively identifies and summarizes three key challenges: \"Efficient data utilization,\" \"Theoretical guarantee,\" and \"Framework generalization.\" The authors propose reasonable solutions to address these challenges. As far as I am concerned, the insights of directly leveraging unlabeled data without predicting missing attributes are straightforward and effective. \n\nThe authors substantiate their claims with theoretical guarantees through Lemma 1 and Lemma 2. Finally, comprehensive experiments are conducted on two representative tasks, highlighting the superiority of FairLISA. In summary, this work exhibits reasonable motivation, a clear literature review, a look-nice model design, and a comprehensive validation experiments.", "weaknesses": "There have been existing works that focus on the complete absence of sensitive attributes, such as [1][2]. Although the specific problems and input data may differ, it would be valuable to explore whether FairLISA can be combined with these works to extend their potential in limited scenarios. This article lacks discussion in these two aspects.\n\n[1]Tianxiang Zhao, Enyan Dai, Kai Shu, and Suhang Wang. Towards fair classifiers without sensitive attributes: Exploring biases in related features. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 2022\n\n[2] Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning, 2018.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ">**A1. Can fairness be extended to settings without sensitive attributes?**\n\nQ1: I sincerely appreciate your question. Currently, our methods cannot be extended to settings without sensitive attributes due to the necessity of labeled data to train the discriminator. However, it is essential to note that our research focuses on fairness with limited sensitive attributes. Our main contribution lies in efficiently utilizing data with both known and unknown sensitive attributes to facilitate fair model training. Additionally, we provide theoretical guarantees, and our experiments demonstrate that our approach achieves SOTA performance. Moving forward, we will explore how to extend our work to settings without sensitive attributes.\n\n\n>**A2. Can the techniques used in settings without sensitive attributes [1][2] be integrated with FairLISA to attain improved outcomes in limited situations?**\n\nQ2: Thank you for your question. The techniques used in settings without sensitive attributes can indeed be integrated with FairLISA, as exemplified by the related work FairRF[1]. FairRF does not explicitly require sensitive attributes and enhances fairness through related features. Building on this core idea, we can seamlessly combine FairRF and FairLISA. Specifically, by leveraging related features, we can infer high-quality labels for sensitive attributes. This data enables us to train our discriminator and filter. Subsequently, these two components can be optimized jointly, potentially leading to improved results. In the future, we will explore how to integrate our method with existing techniques used in settings without sensitive attributes, aiming to achieve improved fairness and performance results. Meanwhile, we will provide more specific discussions in the revision to enhance the clarity and depth of our work.\n\n[1]Tianxiang Zhao, Enyan Dai, Kai Shu, and Suhang Wang. Towards fair classifiers without sensitive attributes: Exploring biases in related features. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 2022\n\n[2] Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning, 2018."}, {"review_id": "WyTONERQn3", "reviewer": "Reviewer_QZCP", "summary": "This paper aims to achieve fair user modeling with limited sensitive attribute information and propose a general framework, FairLISA, which efficiently utilizes data with known and unknown sensitive attributes to facilitate fair model training. The authors also provide theoretical guarantees from a mutual information perspective. Extensive experiments are conducted to demonstrate the effectiveness of FairLISA in scenarios with different ratios of missing sensitive attributes.\n", "questions": "Q1. How does FairLISA perform on classic fairness metrics?\n\nQ2. Please provide the complete experiment setting, including the missing ratio in RQ1.", "limitations": "Although this paper addresses the issue of limited sensitive attribute information, it still requires the collection of some sensitive information as model input, which can potentially compromise privacy. Exploring the combination of fairness and privacy would be a promising direction for future research.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "S1. The scenario of missing sensitive attributes in fair user modeling is both meaningful and worthy of investigation.\n\nS2. The paper is well-written and easy to follow.\n\nS3. The proposed FairLISA efficiently leverages unknown data without the need for predicting missing attributes, providing a simple yet effective approach, supported by theoretical guarantees.\n\nS4. Extensive experiments, especially the RQ2 experiment on different missing ratio situations, demonstrate the effectiveness and robustness of FairLISA in two representative user modeling tasks.", "weaknesses": "W1. This work mainly focuses on the fairness definition where the mutual information between the user modeling result and the sensitive information is zero. However, there are other classic fairness definitions, such as Equalized Odds (EO) and Demographic Parity (DP). It would be valuable to discuss investigate how FairLISA performs on these metrics.\n\nW2. The sensitive information ratio setting is missing in Table 1.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ">**A1. How does FairLISA perform on classic fairness metrics?**\n\n Q2: We have already conducted this experiment in our paper, and the details can be inferred from Experiments RQ4 and Appendix C.6. To provide a clearer presentation of these results, we report the performance of all methods on classical fairness metrics for PMF, LightGCN (representative user models in Recommender System) in Table 1, Table 2 (the lower, the better). The results indicate that our goal of removing the effect of sensitive attributes also benefits the classical group fairness metrics, and our models achieve the best performance. We will highlight this experiment in the revision.\n\nTable 1: The performance on classic fairness metrics for PMF (the lower, the better), the best fairness results are highlighted in bold. \n\n|                 | GENDER DP | GENDER EO | AGE DP   | AGE EO   | OCC DP   | OCC EO   |\n| --------------- | --------- | --------- | -------- | -------- | -------- | -------- |\n| Origin          | 0.106871  | 0.031829  | 0.051489 | 0.027891 | 0.060231 | 0.024581 |\n| ComFair       | 0.054697  | 0.026501  | 0.037224 | 0.028744 | 0.047889 | 0.019412 |\n| FairGo         | 0.029125  | 0.020568  | 0.037694 | 0.028481 | 0.041344 | 0.019123 |\n| FairGNN         | 0.027538  | 0.022540  | 0.023698 | 0.027001 | 0.040135 | 0.019478 |\n| FairLISA | 0.014163  | 0.019857  | 0.020140 | 0.024576 | 0.039625 | **0.018324** |\n| FairGo+FairLISA     | **0.010912**  | **0.017749**  | **0.019480** | **0.023984** | **0.039013** | 0.018352 |\n\nTable 2: The performance on  classic fairness metrics for LightGCN (the lower, the better), the best fairness results are highlighted in bold. \n|                 | GENDER DP | GENDER EO | AGE DP   | AGE EO   | OCC DP   | OCC EO   |\n| --------------- | --------- | --------- | -------- | -------- | -------- | -------- |\n| Origin          | 0.136796  | 0.049581  | 0.060878 | 0.035130 | 0.069747 | 0.029848 |\n| ComFair       | 0.075487  | 0.035767  | 0.057361 | 0.037898 | 0.074596 | 0.022252 |\n| FairGo         | 0.029414  | 0.034892  | 0.057358 | 0.036956 | 0.065387 | 0.022143 |\n| FairGNN         | 0.037538  | 0.045751  | 0.055342 | 0.034956 | 0.064192 | 0.030140 |\n| FairLISA | 0.024163  | 0.022345  | 0.052356 | **0.034596** | 0.062334 | 0.021860 |\n| FairGo+FairLISA      | **0.012912**  | **0.020985**  | **0.052539** | 0.037129 | **0.061489** | **0.021578** |\n\n\n\n\n>**A2. Please provide the complete experiment setting, including the missing ratio in RQ1.**\n\nQ2: Sorry for missing the detail. We will include this information in the revision."}]

Author rebuttal:
We sincerely appreciate all reviewers' time and efforts in reviewing our paper. We would like to thank all of them for providing constructive and valuable feedback which we will leverage to improve this work. Meanwhile, we are encouraged by the positive comments from reviewers, including:

**Motivation:** "Studied an important problem in practical fair recommender system" (Reviewer KpgW),  "an important and practical problem" (Reviewer teqm), "Studying limited sensitive attribute scenario is practical and important."(Reviewer Gbnv), "a socially valuable problem" (Reviewer xseN), "both meaningful and worthy of investigation" (Reviewer QZCP)

**Theoretical Contribution:** "a theoretical-driven perspective" (Reviewer teqm), "supported by the theoretical analysis" (Reviewer Gbnv), "with theoretical guarantees" (Reviewer xseN), "providing a simple yet effective approach, supported by theoretical guarantees." (Reviewer QZCP)

**Method:** "a novel adversarial learning method" (Reviewer teqm), " reasonable solutions" (Reviewer xseN), " straightforward and effective" (Reviewer xseN), "a look-nice model design"(Reviewer xseN), "a simple yet effective approach" (Reviewer QZCP)

**Experimental Results:** "Solid experiments are conducted" (Reviewer teqm), "performance is good based on the empirical evaluation." (Reviewer Gbnv), "a comprehensive validation experiments."(Reviewer xseN), "demonstrate the effectiveness and robustness of FairLISA in two representative user modeling tasks." (Reviewer QZCP)

We have provided specific responses to each reviewer. We hope our responses can clarify all your confusion and alleviate all concerns. We thank all reviewers again. Looking forward to your reply!

Meta-review: Fairness with limited sensitive attribute information is a practically important and relevant setting. The proposed FairLISA framework applies the idea of using unlabeled data to perform fair training. The solution reduces the requirement of providing sensitive information. The reviewers agreed on the rigorousness and soundness of the results. The authors are encouraged to incorporate the discussions incurred during rebuttal in the final version. The camera-ready version can benefit from better positioning the contributions in light of the literature of fair training without sensitive attributes or with only limited and noisy sensitive attributes.
Decision: Accept (poster)