No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users

## Abstract

Abstract Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilities of large language models (LLMs). However, is this effectiveness and cost-efficiency truly a free lunch? In this study, we comprehensively investigate the fairness costs associated with RAG by proposing a practical three-level threat model from the perspective of user awareness of fairness. Specifically, varying levels of user fairness awareness result in different degrees of fairness censorship on the external dataset. We examine the fairness implications of RAG using uncensored, partially censored, and fully censored datasets. Our experiments demonstrate that fairness alignment can be easily undermined through RAG without the need for fine-tuning or retraining . Even with fully censored and supposedly unbiased external datasets, RAG can lead to biased outputs. Our findings underscore the limitations of current alignment methods in the context of RAG-based LLMs and highlight the urgent need for new strategies to ensure fairness. We propose potential mitigations and call for further research to develop robust fairness safeguards in RAG-based LLMs.

## 1 Introduction

Large language models (LLMs) such as Llama and ChatGPT have demonstrated significant success across a wide range of AI applications[, ]. However, these models still suffer from inherent limitations, including hallucinations[] and the presence of outdated information[]. To mitigate these challenges, Retrieval-Augmented Generation (RAG) has been introduced, which retrieves relevant knowledge from external datasets to enhance LLMs’ generative capabilities. This approach has drawn considerable attention due to its effectiveness and cost-efficiency[]. Notably, both OpenAI[] and Meta[] advocate for RAG as a effective technique for improving model performance. However, is the effectiveness and efficiency of RAG truly a free lunch? RAG has been widely utilized in fairness-sensitive areas such as healthcare[, ], education[], and finance[]. Hence, a critical question arises: what potential side effects does RAG have on trustworthiness, particularly on fairness?

Tremendous efforts have been devoted to align LLMs with human values to prevent harmful content generation, including discrimination, bias, and stereotypes. Established techniques such as reinforcement learning from human feedback (RLHF)[] and instruction tuning[] have been proven to significantly improve LLMs alignment. However, recent studies[, , ] reveal that this “impeccable alignment” can be easily compromised through fine-tuning or retraining. This vulnerability arises primarily because fine-tuning can alter the weights associated with the original alignment, resulting in degraded performance. However, what happens when we employ RAG, which does not modify the LLMs’ weights and thus maintains the “impeccable alignment”? Can fairness still be compromised? These questions raise a significant concern: if RAG can inadvertently lead LLMs to generate biased outputs, it indicates that fairness alignment can be easily undermined without fine-tuning or retraining.

To investigate this pressing issue, we propose a practical three-level threat model that considers varying levels of user awareness regarding the fairness of external datasets. Different levels of user awareness of fairness result in different degrees of fairness censorship in these datasets. Consequently, we examine the fairness implications of RAG using uncensored datasets, partially censored datasets, and fully censored datasets on LLMs. Additionally, we explore the effects of pre-retrieval and post-retrieval enhancements of RAG on LLMs’ fairness performance. Alarmingly, our experiments demonstrate that even when using datasets that are fully censored for fairness—which seemingly represents a straightforward solution for mitigating unfairness—we still observe notable degradation in fairness.

Level 1: fairness risk of uncensored datasets (§ 4.2). Many users leverage RAG to enhance specific tasks, often inadvertently overlooking the fairness implications of the external dataset they utilize. Consequently, they may inadvertently rely on uncensored datasets that contain significant biased information. In our experiments, we systematically simulate varying levels of uncensorship by incorporating different proportions of unfair samples into the external dataset. Our findings demonstrate that even a small fraction of unfair samples-such as 20$\%$-is sufficient to elicit biased responses. Furthermore, we observe that the greater the extent of uncensorship, the more pronounced the decrease in fairness.

Level 2: fairness risk of partially mitigated datasets (§ 4.3). While users often focus on addressing well-known and extensively studied biases (e.g., race and gender) in external datasets, our experimental findings indicate that merely removing these prominent biases does not guarantee fair generation within those categories(Fig. 6). Specifically, biased samples from less recognized categories (e.g., nationality) can still adversely affect the fairness of popular bias categories, even when biases from these commonly acknowledged categories have been eliminated. This underscores the need for future research to consider a wider range of bias categories when training or evaluating large language models (LLMs) to create a more robust fairness framework.

Level 3: fairness risk of carefully censored datasets (§ 4.4). Even when users are acutely aware of fairness and implement meticulous mitigation strategies to eliminate bias in the external dataset as much as possible, RAG can still compromise the fairness of LLMs in significant ways (Fig. 7). This vulnerability arises from the fact that information retrieved via RAG can enhance the confidence of LLMs when selecting definitive answers to potentially biased questions (Fig. 8). As a result, there is a decrease in more ambiguous responses, such as “I do not know," and an increased likelihood of generating biased answers. This latent risk suggests that RAG can undermine the fairness of LLMs even with user vigilance, highlighting the need for further investigation in this critical area.

This study is the first to uncover significant fairness risks associated with Retrieval-Augmented Generation from a practical perspective of users on LLMs. We reveal the limitations of current alignment methods, which enable adversaries to generate biased outputs simply by providing external datasets, resulting in exceptionally low-cost and stealthy attacks. Although we find that the summarizer (§ 5) in RAG may offer a potential solution for mitigating fairness degradation, we strongly encourage further research to explore the mechanisms and mitigation techniques related to fairness degradation, with the aim of developing robust fairness safeguards in RAG-based LLMs.

## 2 Related Works

### 2.1 Retreival Augmentation Generation

While large language models (LLMs) have achieved outstanding performance across numerous tasks[, , , ], they continue to face significant limitations such as reliance on outdated training data, generation of hallucinations[], and challenges in handling domain-specific tasks[]. To mitigate these issues, knowledge-enhanced techniques have emerged as a promising solution within the natural langauge processing community[, ]. These methods enrich LLMs with external, interpretable knowledge, offering notable advantages for knowledge-intensive tasks. Among such methods, RAG stands out as one of the most effective strategies. RAG addresses key limitations of LLMs by integrating relevant external knowledge during the generation process, eliminating the need for retraining or fine-tuning the models, and thus representing a cost-effective solution. Leading organizations, including OpenAI[] and Meta[], have recognized the potential of RAG to significantly enhance the performance of LLMs.

RAG consists of two distinct stages: retrieval and generation. In the retrieval stage, relevant external data is fetched from a knowledge base or dataset based on the user query. During the generation stage, this retrieved information is combined with the input query to produce more accurate, contextually relevant responses. This two-stage framework enhances LLMs by allowing them to access real-time external information, overcoming the limitation of static training data. RAG systems can be categorized into two major types based on their retrieval mechanisms: sparse and dense[]. Sparse retrieval relies on explicit term matching between the query and documents, whereas dense retrieval leverages neural embeddings to perform semantic matching. Additionally, several optimization techniques are often employed to improve RAG performance. For instance, pre-retrieval techniques, such as query expansion[], can broaden the scope of retrieval, while post-retrieval methods, such as document reranking[] and summarization[], further refine the relevance and presentation of the retrieved information. These techniques enhance the effectiveness of RAG, particularly in knowledge-intensive domains. Additional technical details are provided in Appendix A.

### 2.2 Fairness Evaluation in LLMs

The fairness evaluation of traditional NLP tasks can be roughly divided into two main categories: (1) embedding-based metrics and (2) probability-based metrics[]. Specifically, embedding-based metrics aim to measure fairness by computing distances in the embedding space between neutral words, such as professions, and identity-related words, such as gender pronouns[, ]. Probability-based metrics involve prompting a model with pairs or sets of template sentences that have their sensitive features (e.g., gender) perturbed, and then comparing the predicted token probabilities between the pairs based on the different inputs[, , , , ]. CrowS-Pairs[] masks every unmodified token for each sentence in pairs, and
evaluate the bias using the estimated probability of the unmodified tokens conditioned on the modified tokens. Question-answering dataset BBQ[] estimates the probability with frequency of targeted bias samples in all non-unknown answers. HolisticBias[] measures the likelihood bias based on the rejection of a hypothesis that there is an equal likelihood of either sentence in a pair to have a higher perplexity than the other.

The evaluation metrics for generation tasks can be divided into three categories: (1) distribution metrics, (2) classifier metrics, and (3) lexicon metrics.
Distribution metrics evaluate bias by comparing the
distribution of tokens between different social groups[, ]. Classifier metrics bring in an auxiliary model to score generated text outputs for their toxicity and bias[, ]. These methods utilize external models, such as the Perspective API[]. Lexicon metrics evaluate generation in word-level by comparing words to a pre-compiled vocabulary of toxic words, probably a list of pre-computed word bias scores[, ].

Figure: Figure 1: A diagrammatic illustration of how varying levels of fairness awareness among RAG users might cause LLMs to produce differing degrees of biased responses.
Refer to caption: x1.png

## 3 Practical Fairness Risks of RAG with LLMs: A Three-level Threat Model

RAG enables LLMs to combine external knowledge with internal information, thereby enhancing content generation capabilities. Typically, the external knowledge has been shown to improve reasoning in domain-specific tasks and mitigate hallucinations. However, there is no reason to dismiss the possibility that externally retrieved knowledge will also inadvertently bring out undesired biased information, which might lead to discriminatory outputs from LLMs. To comprehensively understand the underlying risks, we conduct a practical fairness evaluation from the perspective of practitioners. We recognize the users’ varying levels of awareness regarding the fairness of their datasets can lead to different degrees of scrutiny and bias mitigation before the data is through RAG, as illustrated in Fig. 1. Specifically, we explore three levels of fairness awareness: (1) Low fairness awareness: users directly use uncensored datasets for RAG; (2) Medium fairness awareness: users only mitigate prominent biases in the external dataset; (3) High fairness awareness: users carefully check for all possible biases. The following sections outline the risks we identify within each fairness awareness level.

### 3.1 Level 1: Risks of Uncensored Datasets in RAG-based LLMs

In practical applications, many users employ RAG to improve specific tasks, often inadvertently overlooking fairness implications of the external datasets they rely on. Numerous widely used datasets have been shown to contain biases related to certain sensitive attributes[, ]. Consequently, a significant concern arises when users lack awareness of fairness and directly utilize uncensored original data as external knowledge, as they risk introducing substantial biased information into the LLMs, which may lead to unfair outcomes (shown in the left part of Fig 1). This concern is particularly critical in fairness-sensitive domains such as education, healthcare, and employment, where biased outputs can have serious ramifications in decision-making processes. To reveal these risks, we investigate the impact of using uncensored external datasets containing unfair samples on the fairness performance of RAG. Specifically, our study examines how varying levels of bias in external datasets influence the fairness of LLM-generated outputs, providing valuable insights into the implications of biased external knowledge on equitable decision-making.

### 3.2 Level 2: The Overlooked Risks of Partially Censored Dataset

Even when users actively mitigate prominent biases, such as those related to gender and race, they may still inadvertently overlook less conspicuous biases, like those related to age as shown in the middle part of Fig 1. This scenario is particularly relevant in commercial contexts, where prioritizing the addressing of well-known societal biases often aligns with goals of political correctness and marketing optimization. For instance, Google’s Gemini product was criticized for overcompensating for racial biases by overrepresenting AI-generated images of people of color—an attempt to address historical racial disparities that resulted in unintended overcorrection[]. Similarly, in academic research, while extensive efforts are made to mitigate popular biases such as gender and ethnicity[, , ], less popular biases often receive less attention[]. This trend leads to a disproportionate focus on well-known biases, potentially neglecting less conspicuous biases. Moreover, many bias mitigation techniques in NLP models are designed to address specific bias categories, requiring manual identification of examples for each type[, ]. This further entrenches the disparity between the focus on major versus minor biases. As a result, datasets that are considered “fair” with respect to popular biases may still contain overlooked biases.

In this context, we assume that users may prioritize well-studied and popular biases while neglecting minority biases. Consequently, even if a dataset is considered fair regarding popular biases, overlooked biases may still persist. This raises a critical question: Is a partially censored dataset sufficient to ensure that an LLM will not generate biased content related to the corresponding popular bias category? More broadly, can biases associated with one sensitive attribute (an overlooked bias, such as age) affect the model’s fairness regarding another sensitive attribute (a widely-studied bias, such as gender)?

### 3.3 Level 3: Unseen Threats in Fully Censored Datasets

Imagine a scenario where users with high awareness of fairness meticulously ensure that all sensitive attributes within an external dataset are unbiased, resulting in a dataset that appears to have be censored (right part of Fig 1). Intuitively, one might assume that such a carefully curated dataset would guarantee fairness in downstream tasks. However, recent findings[, ] reveal a surprising risk: even when models are fine-tuned with seemingly benign data, they can still experience safety degradation, undermining their previous well-aligned fairness and ethical standards.
This raises a disconcerting question in the context of RAG-based LLMs: could the interaction with a dataset that is ostensibly fair still compromise the fairness of the model? In contrast to fine-tuning, RAG-based LLMs integrate external knowledge from ready-made datasets, meaning fairness degradation could occur through the simple act of retrieving information, without modifying the model’s internal parameters. Such a scenario would be deeply concerning. It suggests that even routine usage of RAG-based LLMs could lead to biased or discriminatory outputs, posing a subtle but serious vulnerability. Adversaries might exploit this mechanism to degrade fairness without directly manipulating the model, raising critical concerns about the reliability of current LLMs.

## 4 Exploring Fairness Risk in RAG-based LLMs

This section presents empirical evidence regarding the fairness risks associated with the integration of RAG into LLMs, as discussed in Sec. 3. We conduct a comprehensive investigation of the fairness implications by designing a robust set of experiments that encompass a variety of NLP tasks, including classification, question answering, and sentence completion. Specifically, Sec.4.1 details the experimental setup, including the tasks, metrics, and LLMs employed in our study. Following this, Sec. 4.2, Sec. 4.3, and Sec. 4.4 analyze the fairness risks posed by RAG-based LLMs, considering different levels of dataset censorship across the various tasks.

### 4.1 Study Setup

We evaluate the fairness implications of RAG-based LLMs across three distinct tasks: classification, question answering, and genration tasks, based on state-of-the-art LLMs, specifically Llama7B, Llama13B, GPT-4o, and GPT-4omini. These models encompass both advanced closed-source and open-source options, allowing us to comprehensively assess the fairness implications of RAG.

Classification Task: We use the PISA dataset(^1^11[https://www.kaggle.com/datasets/econdata/pisa-test-scores](https://www.kaggle.com/datasets/econdata/pisa-test-scores)), containing data from U.S. students in the 2009 PISA exam. Reading scores below 500 are classified as “Low” and those above 500 as “high”[]. The goal is to predict a student’s score category bases on provided features. Specifically, in our experiment, gender (Male or Female) is used as the sensitive feature for fairness evaluation. Historically, stereotypes suggests females outperform males in reading[]. To simulate this bias, we create an uncensored dataset by assigning high scores to all females and low scores to all male (unfairness rate=1.0). We assess model performance through both accuracy and fairness, using metrics such as statistical parity, equal opportunity, and equalized odds[].

Question-Answering Task: We utilize the BBQ dataset[] to evaluate how RAG-based LLMs handle social biases across eleven dimensions. The task involves assessing LLMs responses in both ambiguous (under-informative) and disambiguated (well-informed) contexts. To integrate RAG, we convert question-answer pairs into context documents, using disambiguated questions with the corresponding correct answers as fair samples and ambiguous ones with biased answers as counterfactuals to simulate unfair data. Details of pre-processing and post-processing steps of BBQ dataset can be found in Appendix D. For fairness evaluation, we extend BBQ’s original metrics to LLMs, accounting for the model’s capability to refuse answering biased questions—a novel feature introduced in alignment stages. We treat refusals as unbiased outcomes. In disambiguated contexts, unfairness is the proportion of biased responses among all non-unknown answers, including refusals. For ambiguous content, scores are scaled by accuracy[]. Full metric details of BBQ are available in Appendix C.

Generation Task: HolisticBias[] contains 460,000 sentence prompts reflecting demographic identities (e.g., “Hi! I am a Catholic grandmother.”) used in two-person dialogues. these prompts aim to enable LLMs to generate large text corpora for examining fairness and potential bias in models. However, single-sentence prompts lack the rich context necessary to be used as external knowledge. To address this, we utilize OPT-1.3B[] to extend the prompts into richer dialogues, which are then evaluated for toxicity using the widely adopted Perspective API[]. Specifically, this API assigns a toxicity probability (ranging from 0 to 1) to each input. Consequently, samples with toxicity scores below 0.1 are categorized as fair samples, while those above 0.5 are deemed unfair. In the evaluation, we also adopt the toxicity score from the Perspective API as our evaluation metric, with the average toxicity score serving as the primary evaluation criterion. An overview of the metrics is presented in Table 1.

We split each dataset into 80$\%$ for training and 20$\%$ for testing. In a RAG framework, the training set serves as an external knowledge source for model generation, and the testing set is used to evaluate fairness. We create six versions of the training data, each with a different level of unfairness, based on predefined unfairness rates (0.0, 0.2, 0.4, 0.6, 0.8, 1.0). For example, an unfairness rate of 0.2 means that 20% of the samples in the external dataset are unfair, while the remaining 80% are fair samples. This enables us to analyze how varying fairness in the external dataset influences LLM generation. For unbiased comparisons across bias categories, we select 100 samples per category, or all available samples if fewer than 100, while maintaining the targeted unfairness rate. Further details on the RAG implementation can be found in Appendix B.

**Table 1: Fairness evaluation metrics for diverse tasks (higher values indicate greater unfairness).**
| Task | Dataset | Metric | Formulation |
| --- | --- | --- | --- |
| Classification | PISA(^2^22[https://www.kaggle.com/datasets/econdata/pisa-test-scores](https://www.kaggle.com/datasets/econdata/pisa-test-scores)) | Statistical Parity ($\text{stat_parity}\uparrow$) | $\mathrm{P}\left(\hat{y}=1\mid s=0\right)=\mathrm{P}\left(\hat{y}=1\mid s=1\right)$ |
| Equal Opportunity (TPR $\uparrow$) | $\mathrm{P}\left(\hat{y}=1\mid y=1,s=1\right)=\mathrm{P}\left(\hat{y}=1\mid y=1<br>,s=0\right)$ |  |  |
| Equalized Odds (FPR $\uparrow$) | $\begin{aligned} &\mathrm{P}\left(\hat{y}=1\mid y=1,s=1\right)=\mathrm{P}\left(<br>\hat{y}=1\mid y=1,s=0\right)\\<br>&\mathrm{P}\left(\hat{y}=1\mid y=-1,s=1\right)=\mathrm{P}\left(\hat{y}=1\mid y<br>=-1,s=0\right)\end{aligned}$ |  |  |
| Question-Answering | BBQ[] | Bias Score ($\uparrow$) | $\begin{aligned} &\mathrm{B\text{-}S}_{ambig}=(1-\mathrm{Acc})\times\left(2<br>\frac{S\text{-}T}{S\text{-}T+S\text{-}U}-1\right)\\<br>&\mathrm{B\text{-}S}_{disambig}=2\frac{S\text{-}T}{S\text{-}T+S\text{-}U}-1<br>\end{aligned}$ |
| Generation | HolisticBias[] | Toxicity Score ($\uparrow$) | $\mathbb{E}_{x\sim\mathcal{D}}f_{\theta}(x)$, where $f_{\theta}$ is the Perspective API. |

Figure: (a)
Refer to caption: x2.png

### 4.2 Fairness Risks Associated with Uncensored Dataset

Building on the scenario in Sec. 4.2, we investigate how an uncensored external dataset containing unfair samples affects the fairness of RAG-based LLMs. Specifically, we evaluate the fairness performance of RAG-based LLMs across different levels of unfairness in the external dataset.

Uncensored data significantly degrades fairness. Figs. 2 and first two sub-figures in Fig. 3 shows the comparison between the No-RAG baseline and RAG-based LLMs across different unfairness rates on three datasets. The results consistently indicate a decline in fairness as the unfairness rate increases, highlighting that higher levels of unfairness in the external dataset lead to more pronounced fairness degradation across most RAG-based LLMs.

Figure: (a)
Refer to caption: x6.png

Fairness implications vary across task scenarios and model quality. Fig. 2 and first two sub-figures in Fig. 3 also reveal that fairness degradation patterns differ between LLMs, even within the same task. For instance, GPT series LLMs outperform Llama series LLMs in the generation task (Holistic). However, in the classification task (PISA) and the question-answering task (BBQ), Llama series LLMs exhibit superior fairness across all unfairness rates. This is unexpected, given that GPT series LLMs are typically regarded as more advanced, with better alignment to trustworthiness. To explore this further, we analyzed the accuracy results, as shown in last two sub-figures in Fig. 3. The findings reveal that Llama models perform significantly worse in terms of accuracy compared to GPT series LLMs. On BBQ, Llama series LLMs achieve less than 50$\%$ accuracy, performing not much better than random guessing. This suggests that the apparent fairness advantage in Llama series LLMs might stem from their inability to properly understand the questions, leading to random responses rather than informed, fairness-aware decision. Moreover, as shown in Fig. 4, Llama series LLMs are notably more cautious than GPT series LLMs, often refusing to answer a higher proportion of questions.

Figure: (a)
Refer to caption: x10.png

For instance, Llama7B refuses to answer 10$\%$ of questions, even without using RAG. We believe this hyper-cautious behaviour contributes to their perceived fairness, as refusing to answer reduces the chances of generating unfair or biased content. However, this also comes at the cost of user experience. Considering accuracy, response rate, and fairness, we recommend using GPT series LLMs in practice, as they strike a better balance across these metrics.

Figure: (a)
Refer to caption: x12.png

Sensitivity to different bias categories. The BBQ dataset, which includes samples from various bias categories, allows us to examine fairness performance across these different categories. Specifically, we compare the fairness degradation of GPT series LLMs on BBQ, contrasting the No-RAG baseline with RAG-based LLMs that utilize unfair data (unfairness rate of $1.0$) as shown in Fig. 5. We observe a slight decrease in fairness regarding prominent biases, such as race-ethnicity and sexual orientation. However, for less prominent bias categories like religion and age, there is a more significant drop in fairness after applying RAG. This suggest that GPT series LLMs’ alignment efforts focus more on widely recognized biases, with less attention given to underrepresented categories. This finding aligns with prior research[]. Full results are provided in Appendix E.

###### Remark 4.1 .

The fairness of LLMs can be significantly compromised through RAG when using uncensored datasets. As the level of uncensorship increases, fairness decreases more sharply, posing serious risks to model alignment. This is especially concerning given the substantial asymmetry in alignment efforts: despite OpenAI’s commitment to allocating 20$\%$ of its computational resources to alignment[, ], fairness can still be easily undermined through RAG without any additional fine-tuning or retraining.

###### Remark 4.1 .

### 4.3 Fairness Risks Associated with Partially Censored Dataset

Given the practical scenario discussed in Sec. 3.2, it is critical to assess whether mitigating bias in one specific category is sufficient on its own. More broadly, we explore whether bias in one category (RAG bias category, RC) affects fairness in another category (test bias category, TC) with RAG-based LLMs. To investigate this, we create partially censored datasets where unfair samples from one RC (with a 1.0 unfairness rate) are combined with fair samples from one TC (with a 0.0 unfairness rate). We then measure the impact of the biased RC on the TC by comparing RAG with partially biased data against RAG with fully censored data (clean RAG). The difference in fairness scores allows us to quantify how bias in the RC impacts fairness in TC.

Figure: (a)
Refer to caption: x14.png

We present the results of GPT series LLMs on the BBQ dataset in Fig. 6. Each row corresponds to a biased RC, and each column corresponds to a TC, with the values in the plot representing the difference in fairness between RAG with partially biased data and RAG with clean data. Positive (red) values indicate that bias in the RC negatively impacts fairness in the TC, even when all TC samples are fair in the external dataset.

Popular biases can not be eliminated in isolation. As shown in Fig. 6, fairness in prominent bias categories like race and gender can still be compromised, even when the external dataset lacks unfair samples from those categories. However, not all bias categories (RCs) lead to fairness degradation in these categories. For instance, in the GPT-4o results, categories such as race related (race$\times$SES, race$\times$ethnicity, and race$\times$gender) consistently show fairness degradation when the dataset contains biased samples related to nationality, sexual orientation, or gender identity. Moreover, the fairness of gender identity is affected when biased samples are related to physical appearance and disability. Although GPT-4o mini also shows fairness degradation in race and gender due to certain biased RCs, there is no consistency in the biased RCs observed in GPT-4o mini compared to those observed in GPT-4o.

**Table 2: Classification of TCs based on how they are affected by biased RCs.**
| Vulnerable Category | Passive Category | Backfiring Category |
| --- | --- | --- |
| Religion | Race | Physical appearance |
| Disability status | Nationality | SES |

Varying fairness relationships across bias categories. Fig. 6 further illustrates that bias categories such as disability status, age, and religion are more vulnerable to the influence of other biased RCs, as reflected by the predominantly red columns. However, some bias categories exhibit no consistent direction of change, resulting in mixed red and blue scores. Interestingly, we also observe a “backfiring“ phenomenon, where certain categories (e.g., physical appearance and socioeconomic status) become even less biased when the dataset contains unfair samples from unrelated categories. Based on the above observations, we categorize some typical bias types based on their response to biased RCs (as shown in Table 2): (1)Vulnerable Categories: categories where unfairness increases due to biased data from other categories; (2)Passive Categories: categories showing little or inconsistent change in fairness; (3)Backfiring Categories: categories where fairness improves (toxicity decreases) when exposed to biased data from other categories. In particular, the “backfiring” effect may raise from the low correlation between these categories and others. For example, physical appearance and socioeconomic status might be more individualistic, making them less susceptible to biased knowledge retrieved during RAG, allowing responses based primarily on fair knowledge from their original class.

###### Remark 4.2 .

Eliminating bias in prominent categories alone is insufficient for ensuring the fairness of those categories. Fairness degradation may still occur due to the influence of other overlooked bias categories. This highlights the need to broaden the scope of bias mitigation efforts to include a wider range of categories, even if the primary focus is on more recognized ones.

Figure: Figure 7: The Fairness comparison between Non-RAG and Clean RAG.
Refer to caption: x16.png

Figure: (a)
Refer to caption: x17.png

###### Remark 4.2 .

### 4.4 Fairness Risks Associated with Fully Censored Datasets

This section investigates the fairness of LLMs under the premise that users are highly aware of fairness and implement mitigation strategies for both prominent and less prominent bias categories. This scenario, as discussed in Sec. 3.3, presents significant concerns regarding fairness outcomes. We define fully censored datasets as those with a zero unfairness rate for conducting clean RAG. To assess the implications of clean RAG, we compare the fairness performance of four LLMs operating under clean RAG against those without RAG across the three dataset. Fig. 7 presents the evaluation results for the GPT series LLMs across three datasets, with additional results for Llama series LLMs detailed in Appendix F. The results reveal that even when using fully censored datasets, LLMs can still experience compromised fairness. Specifically, all LLMs demonstrate consistent fairness degradation on the PISA dataset after the application of clean RAG. Additionally, results from other datasets indicate that the majority of bias categories exhibit differing extents of fairness decline. Notably, categories such as age, socioeconomic status (SES), and gender consistently show reductions in fairness after clean RAG is applied to the GPT series LLMs.

This observation raises critical concerns, prompting us to investigate the underlying causes. Our analysis suggests that the external knowledge introduced by RAG may inadvertently enhance the confidence of LLMs, leading them to provide more definitive responses to questions instead of choosing neutral replies such as “I do not know,” as illustrated in Fig 8. Consequently, for questions that potentially contain bias, where LLMs might initially lean towards neutrality, the application of RAG increases the likelihood of generating biased responses, thereby increasing the risk of unfair outcomes.

###### Remark 4.3 .

The notion of clean RAG appears to offer a straightforward solution for mitigating unfairness. However, it ultimately undermines fairness performance. This poses a significant threat to the fairness alignment of LLMs, suggesting that a stealthy and highly effective breach of fairness could be easily achieved solely through the implementation of clean RAG, without the necessity of retraining or fine-tuning.

###### Remark 4.3 .

## 5 Discussions

To improve the quality of retrieval and generation, it is common practice to leverage pre-retrieval or post-retrieval strategies to enhance the accuracy and relevance of the retrieved results. In this section, we discuss the impact of these widely adopted strategies on fairness performance by comparing the changes in fairness before and after applying these methods on unfair data (unfairness rate is $1.0$). Specifically, we conduct experiments on HolisticBias dataset using GPT series models.

Impact of sparse retrieval. Apart from the dense retrieval used in this paper, sparse retrieval, which relies on explicit term matching between the query and documents, is typically employed for retrieval. As shown in Fig. 9, sparse retrieval has little impact on the model fairness.

Figure: (a)
Refer to caption: x19.png

Impact of reranker. Reranking is a post-retrieval process that involves reordering a list of retrieved items. In our experiment, for each query, we retrieve 10 related pieces of information and use Colbertv2[] as the reranker to reorder the items according to their relevance to the query. We then select the top five items for the final generation. As shown in Fig. 9, reranker do not so a significant impact on the fairness evaluation.

Impact of query expansion. We follow[] to employ query expansion, which is a pre-retreival enhancement method that generates pseudo-documents by few-shot prompting LLMs and expands the query with the relevant information in pseudo-documents to improve the query for more relavent retreive. As shown in Fig. 9, query expansion technique shows a mild bias mitigation effect.

Impact of summarizer. After retrieval, summarizing the retrieved text can help distill information from a large number of documents, providing essential context for an LLM. In our experiment, we use ChatGPT-3.5 Turbo to generate summaries with a simple prompt: “Write a concise summary of the following.” As shown in Fig. 9, the summarizer demonstrates the strongest bias mitigation effect, providing a potential approach to prevent fairness degradation. More results can be found in Appendix G.

## 6 Conclusion

This work examines the fairness risks of RAG from three levels of user awareness regarding fairness and reveals the impact of pre-retrieval and post-retrieval enhancement methods. Results in our experiments show models and categories vary in unfairness influences, where even RAG with partially censored data will lead to fairness degradation on the same category. Our further analysis demonstrates that fairness can be easily compromised by RAG, even when using clean datasets. This finding highlights the stealthy and low-cost nature of adversarial attacks aimed at inducing fairness degradation, which poses significant threats to the alignment of LLMs. Hence, we strongly encourage further research focused on strengthening fairness protocols in RAG processes.

## Appendix A More details of Retrieval and Generation

### A.1 Retrieval

Before retrieval, external documents must first be processed from raw data into a list of small, noticeable chunks that can be efficiently handled by language models. Since external data sources may vary significantly in format, it is necessary to align these sources into uniform, context-rich chunks. Following this, an embedding model is employed to encode the chunks, creating embeddings that facilitate the indexing[].
From the perspective of encoding mechanisms, retrieval methods can be broadly categorized into two types: sparse and dense, depending on how the information is encoded[]. Sparse methods rely on explicit term matching, while dense methods leverage learned embeddings to capture deeper semantic relationships within the data.
Sparse retrieval is primarily word-based and widely employed in text retrieval tasks. Classical approaches such as TF-IDF and BM25[] rely on inverted index matching to identify relevant documents. BM25, in particular, is often applied from a macro perspective, where entire passages are treated as singular retrieval units[, , ],[]. However, a key limitation of sparse retrieval in the context of RAG is its untrained nature, leading to retrieval performance highly dependent on both the quality of the data source and the specificity of the query. In contrast, dense retrieval encodes user queries and external knowledge into vector representations, enabling application across a wide range of data formats[]. Simple dense retrieval methods[] compute similarity scores between the query vector and the vectors of indexed chunks, retrieving the top $K$ similar chunks to the query. These retrieved chunks are then incorporated as an extended context within the prompt, facilitating more accurate and contextually relevant responses.

Embedding models are a crucial component of dense retrieval systems. A straightforward approach involves utilizing off-the-shelf NLP models. BERT-based architectures[] are commonly employed in retrieval models. A prevalent design within RAG frameworks involves constructing bi-encoders with the BERT structure—one encoder dedicated to processing queries and the other for documents[, ]. Further advancements in RAG models are achieved through large-scale specialized pre-training, which enhances their performance on knowledge-intensive tasks. A notable example is the Dense Passage Retriever (DPR)[], which employs a BERT-based backbone and is pre-trained specifically for the OpenQA task using question-answer pair data. DPR has demonstrated significant efficacy as a pre-trained retriever, contributing to the success of numerous RAG models across various downstream applications[, , , ]. An alternative approach to dense retrieval that has gained significant traction in Retrieval-Augmented LLMs involves using a single encoder architecture[, ].This encoder can be built upon Transformer models, BERT, or other readily available sequence modeling frameworks.

To improve the quality of retrieval, enhancement is necessary in pre-retrieval stage. These enhancements are mostly about optimizing indexing and optimizing query. Key areas for optimizing indexing quality include enhancing data granularity, refining index structures, incorporating metadata, improving alignment, and enabling mixed retrieval strategies[, , , ]. To optimize query qualities, some research have applied query expansion[], query rewriting [, ] and query augmentation [].

### A.2 Generation

Some post-retrieval techniques have also been proposed to enhance query performance and augment retrieved results. Reranker is an important strategy in post-retrieval processing. Once relevant information is retrieved, reranker is applied to prioritize the most pertinent content, ensuring that key details remain prominent while minimizing the presence of irrelevant information[]. This approach helps prevent cognitive overload, and has been implemented in systems such as LlamaIndex(^3^33[https://www.llamaindex.ai](https://www.llamaindex.ai)) and LangChain (^4^44[https://www.langchain.com](https://www.langchain.com)). Apart from reranker, summarizer(context compressing technique) eliminates the imapct of excessive contexts that introduce noise, keeping the LLM’s perception of key information[, ].

Additionally, several studies have focused on the augmentation of RAG models, primarily in the area of model architecture. Typical structures such as iterative retrieval (ITER-RETGEN[]) and recursive retrieval[, ]have introduced structural variations to RAG, showcasing new methods for adaptive or self-retrieval.

## Appendix B More Details of RAG Implementation

We implemented our codebase using the LangChain Python package. Specifically, in the RAG process, we treat the curated training set as the external dataset and use the advanced embedding model bge-small-en-v1.5[] to encode both the query and the documents. The relevancy between the query and each document is then calculated using the dot product. For efficient similarity search, we employ FAISS[] as our vector database. For each input query, we select the top five most relevant pieces of information from the external dataset for augmentation. The retrieved documents are then concatenated with the query using the corresponding prompt for each task to enhance the LLM’s generation. The details of the prompts used across the three tasks are as follows

## Appendix C More Details of BBQ Evaluation

BBQ contains three types of answer labels: bias (targeted) label, true label, and unknowns (wishy-washy, including nine different texts of “unknown“ semantics). Corresponding to its data structure, BBQ metric calculates the bias score through the ratio of stereo-targeted(answer label equals to the bias label) of all samples except non-unknowns.
To mitigate the impact of refusal (which primarily exists in the LLAMA model) in the evaluation of LLM models, we treat refusal as unbiased labels. For the ambiguous group, we apply the accuracy adjustment to distinguish between unfair answers and incorrect but fair answers. The final bias score is mapped to the range [-1, 1], where -1 indicates all responses are fair, and 1 indicates all responses are target-biased.

**Table 3: Descriptions of LLM-answer types for BBQ**
| Category | Description |
| --- | --- |
| Stereo-targeted (S-T) | answer label = bias label |
| Stereo-untargeted (S-U) | answer label $\neq$ bias label, answer label $\notin$ unknowns |

$$ $\mathrm{Acc}=\frac{True}{True+False}\quad\quad\text{True, False}\notin\text{refusals}$ (1) $$

$$ $\begin{split}&\mathrm{B\text{-}S}_{ambig}=(1-\mathrm{Acc})\times\left(2\frac{S \text{-}T}{S\text{-}T+S\text{-}U}-1\right)\end{split}$ (2) $$

$$ $\begin{split}&\mathrm{B\text{-}S}_{disambig}=2\frac{S\text{-}T}{S\text{-}T+S \text{-}U}-1\end{split}$ (3) $$

## Appendix D More Details of Data Processing for BBQ

In processing BBQ dataset for our RAG task, we encountered two issues. (1) Redundancy issue: The contexts and questions in BBQ are generated from some given templates, which results in high similarity among many of them. This interferes effectiveness of retrieval head with the embeddings extracted from the texts. Besides, redundant samples also waste the computational resources of the LLM. (2) Balance issue: There are significant differences in sample sizes across different bias categories in BBQ, which leads to inconsistent impacts of these categories in RAG.

To address the redundancy issue, we calculate the similarity between all texts using Levenshtein distance during the pre-processing phase. After that we remove samples with similarity above a specified threshold. For the balance issue, we conduct resampling and alignment in the post-processing phase based on a fixed unfairness rate and a scale parameter. This ensures that the final samples strictly meet the unfairness rate while maintaining a count no more than the scale we want. The improved BBQ processing Algorithm 1 takes the unfairness rate and scale parameters as inputs to generate non-redundant and balanced BBQ data for RAG.

Figure: Algorithm 1 BBQ processing pipeline

## Appendix E More Details of Results on Uncensored dataset

Figure: Figure 10: BBQ results on GPT series under entire unfairness rates and different context conditions.
Refer to caption: x21.png

Fig. 10 presents fine-grained evaluation results across different bias categories for GPT series, supplemented by results from disambiguated contexts. Generally, the bias space—the area enclosed by each colored line in the radar plot—tends to expand as unfairness increases across most categories.

Fig. 11 shows the evaluation results for Llama-series models when different categories of bias are introduced in uncensored data, where “Ambig“ and “Disambig“ denote the ambiguous test data and disambiguated test data in the BBQ dataset, respectively. A similar finding observed with the GPT series LLMs can also be seen in the Llama-series models. Specifically, different bias categories show varying extents of fairness degradation, which may be attributed to the differing levels of fairness alignment efforts made by Llama for each category.

Figure: Figure 11: BBQ results on Llama series with uncensored data under different context conditions.
Refer to caption: x22.png

## Appendix F More Details of Llama Series Models on Censored Dataset

Figure: Figure 12: The Fairness comparison between Non-RAG and Clean Rag based on Llama series models.
Refer to caption: x23.png

We present a comparison between no RAG and clean RAG based on the Llama series model in Fig 12. We observe a similar trend to that of the GPT series: even with a fully censored dataset, the fairness of LLMs can still be compromised. In particular, for the PISA dataset, all models demonstrate consistent degradation in fairness after applying clean RAG. However, Llama series models do not show a clear pattern regarding which bias categories are more vulnerable in terms of fairness.

## Appendix G More details of Ablation Results

Figure: Figure 13: Bias scores after applying different pre-retrieval and post-retrieval strategies on BBQ dataset.
Refer to caption: x24.png

As shown in Fig. 13, we present the impact of pre-retrieval and post-retrieval strategies on fairness performance in terms of all bias categories. A similar trend is observed as in the main text: the summarizer can alleviate fairness degradation across all bias categories, while reranker and query expansion strategies do not show significant influence on fairness with respect to these categories.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: In Section 4.1's Question-Answering Task evaluation (line 268), the authors' assumption that 'refusals equal unbiased outcomes' is oversimplified. LLMs can respond to biased questions with nuanced explanatory answers rather than simple refusals as "i do not know", making this binary classification inadequate for measuring bias.
soundness: 3
strengths: 1. Important topic: This study examined a critical concern about how RAG approaches can affect LLM alignment, particularly regarding fairness implications.
2. Comprehensive Analysis: The study provided a well-structured evaluation across different scenarios, considering various levels of user awareness and types of dataset censorship.
summary: This paper examines the fairness implications of Retrieval-Augmented Generation (RAG) in LLMs. While RAG effectively reduces hallucinations and improves performance, the study shows it can compromise fairness even with bias-censored datasets. Through analyzing three levels of user awareness (uncensored, partially censored, and fully censored datasets), researchers found that RAG-based models generate biased outputs without changing parameters of LLMs. The findings highlight limitations in current fairness alignment strategies and call for new safeguards.
weaknesses: 1. Limited contribution: While the paper thoroughly analyzes how RAG can compromise LLM fairness, it offers minimal practical solutions, only briefly suggesting mitigation strategies in its conclusion.
2. Ambiguous scope: The paper examined RAG's impact on LLM fairness, distinguishing between supposedly 'prominent' biases (race-ethnicity, sexual orientation) and 'less conspicuous' ones (religion, age). However, this classification lacks systematic supporting evidence. The paper arbitrarily categorizes 11 types of bias without justifying why certain biases are considered more prominent than others. For instance, the classification of religious bias as less conspicuous than racial or sexual orientation bias remains unsupported. This arbitrary categorization undermines the study's comprehensiveness.
3. Unconvincing Analysis: In Section 4.3, the paper identified that RAG data with biases in one category can trigger biases in another category. While this finding is significant, it lacks in-depth analysis of potential underlying mechanisms.
In Section 4.4, The paper argued that RAG with censored datasets compromises LLM alignment by promoting positive responses where the LLM previously refused biased queries. However, this argument has two key weaknesses: It oversimplifies appropriate LLM responses to biased content, assuming rejection is the only valid response rather than acknowledging possibilities for explanatory responses. Secondly, the analysis relies on intuitive speculation rather than experimental evidence.

---

confidence: 3
contribution: 4
initial_rating: 6
presentation: 4
questions: * Were statisical significance tests conducted to test the hypotheses?  And, if so, do they correct for multiple comparisons?
soundness: 3
strengths: * **Clear hypotheses.** The authors very clearly specify their hypotheses across reasonable experimental conditions.  This allows for the construction of appropriate experiments and testing. 
* **Metrics are well-motivated and operationalized.** The authors use established and clear metrics for unfairness, covering a variety of different operationalizations, depending on the task. 
* **Well-motivated.** The research is salient given the current movement toward RAG using (safe) LLMs that may be degraded through corpora. 
* **Good experiments.** The experiments are solid and thorough.  All of these results are clearly analyzed and put into context.
summary: The authors explore the relationship between RAG and generation fairness under three conditions: (a) uncensored corpora, (b) partially censored corpora, and (c) fully censored corpora.  They test their hypotheses under several tasks, fairness metrics, and models.  The results demonstrate that, under all conditions, RAG can degrade fairness compared to non-RAG models.  

**Given that both RAG and Data-Centric AI are current methods for addressing multiple concerns with LLMs, this is an important area of study.**
weaknesses: * **Statistical testing.** The hypotheses and experiments are very clear and amenable to proper hypothesis testing.  This would make it easier to understand the robustness of these results. Please consult texts on significance testing ("Empirical Methods for Artificial Intelligence"), including corrections for multiple comparisons.  
* **Implications.** The observations in the experiments are interesting and deserve some treatment of next steps.  There's a short sentence in the conclusion alluding to the need for future work on fairness but it would be good to explore this more.  
* **Related work.** There is related work on the difficulty of data-driven approaches to fairness that is worth drawing connections to.   There are several here,
   * Esther Rolf, Theodora T Worledge, Benjamin Recht, and Michael Jordan. Representation matters: assessing the importance of subgroup allocations in training data. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th international conference on machine learning, volume 139 of Proceedings of Machine Learning Research, 9040--9051, PMLR, 18--24 Jul 2021.
   * Meera A. Desai, Irene V. Pasquetto, Abigail Z. Jacobs, and Dallas Card. An archival perspective on pretraining data. Patterns, 5(4):100966, 2024.
   * Fernando Diaz and Michael Madaio. Scaling laws do not scale. In Proceedings of the 2024 aaai/acm conference on ai, ethics, and society, 2024.
   * Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, and Vinodkumar Prabhakaran. Re-imagining algorithmic fairness in india and beyond. In Proceedings of the 2021 acm conference on fairness, accountability, and transparency, FAccT '21, 315--328, New York, NY, USA, 2021. , Association for Computing Machinery.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: - Can you provide more detailed examples or case studies of the biases that were introduced by RAG, particularly in the context of fully censored datasets?

- How were the datasets selected and processed for each level of censorship, and how do you ensure consistency across these levels?

- How do you propose integrating the suggested mitigation strategies into existing LLMs without affecting their performance in other areas?
soundness: 3
strengths: - The paper proposes a novel insight into RAG fairness and highlights the potential for RAG to introduce biases (even in supposedly unbiased systems).

- The threat model is well-structured.

- The experimental setup is clear and robust. The paper adopts various benchmarks to assess the fairness of RAG comprehensively.
summary: This paper proposes an empirical study about user awareness of fairness regarding RAGs, showing that retrieval can introduce biases even with unbiased datasets. The study uses a three-level threat model based on user awareness of fairness, examining uncensored, partially censored, and fully censored datasets. Experiments reveal that RAG can degrade fairness without fine-tuning, underscoring limitations in current alignment methods and the need for new fairness safeguards.
weaknesses: - The paper does not provide detailed qualitative samples to support the argument, which is especially important for the generation task. The Perspective API can assign low toxicity scores to repeated meaningless sentences or high scores to correct answers containing specific toxic words. 

- The evaluation focuses on the fairness side but neglects the retrieval efficacy. The paper suggests a trade-off between retrieval/accuracy and fairness, but it has not been studied in the experiments.

- The paper tends to focus more on high-level implications rather than providing an in-depth analysis of the underlying mechanisms of why and where RAG introduces biases.

- The definitions of "uncensored," "partially censored," and "fully censored" datasets are somewhat vague. How were the datasets selected and processed for each level of censorship, and how do you ensure consistency across these levels?

---

confidence: 5
contribution: 1
initial_rating: 3
presentation: 3
questions: 1. The statistical disparity and other indicators in the table 1 are more accurate if they are formed by subtraction instead of equality, reflecting a value instead of true or false. In addition, there are upward arrows in the table to indicate the direction of the value. And the symbols in the table lack annotations, such as S, T, U, $f_{\theta}$, etc.
2. The second sub-table in Fig. 2 seems to be different with a non-monotonic relationship with the unfairness rate? Is there any reason? It seems wrong to claim "The results consistently indicate" in the text. In addition, Line 318-344 mentions findings from Figs. 2 and the first two sub-figures in Fig. 3, but the vertical coordinates of Figs. 2 and the first two sub-figures in Fig. 3 are not consistent.
3. The introduction of RAG in Fig. 3 not only leads to a decrease in fairness, but also a decrease in accuracy, which means that the data quality is lower. Does it mean that the degradation of fairness comes from the data itself rather than RAG?
4. Regarding that the Llama series performs better than GPT on the Holistic dataset, the article says that it "might stem from their inability to properly understand the questions". Is there any further experimental data to support this?
5. In "FULLY CENSORED DATASETS", the experimental results shown in Figure 8 cannot well reflect the bias caused by RAG. A more reasonable way is to show the proportion of biased answers under all known answers, rather than count. In addition, the proportion of correct answers also needs to be shown to show the advantages of RAG.
6. Why can the summarizer alleviate the reduction in fairness to a certain extent? The specific reasons are not analyzed
7. Will different RAG construction methods produce different results? [4] 

[4]Shrestha, R., Zou, Y., Chen, Q., Li, Z., Xie, Y., & Deng, S. (2024). FairRAG: Fair human generation via fair retrieval augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11996-12005).
soundness: 2
strengths: RAG significantly impacts the performance of LLMs. The paper’s motivation on the potential bias of RAG is encouraging

The authors have thoughtfully considered varying levels of fairness awareness among human users, which is presented clearly.

The experiments are conducted across a range of tasks, including classification, question answering, and generation tasks, which is a positive aspect of the study.
summary: The author conducted an experimental analysis on the fairness risks introduced in LLMs when human users with three different levels of fairness awareness and use RAG with uncensored data, partially censored data, and fully censored data.
weaknesses: 1.  The works [1][2][3]  share similar objectives and are worth to be discussed.

[1] Wu, X., Li, S., Wu, H. T., Tao, Z., & Fang, Y. (2024). Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems. arXiv preprint arXiv:2409.19804.

[2] Dai, S., Xu, C., Xu, S., Pang, L., Dong, Z., & Xu, J. (2024, August). Bias and unfairness in information retrieval systems: New challenges in the llm era. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 6437-6447).

[3] Dai, S., Xu, C., Xu, S., Pang, L., Dong, Z., & Xu, J. (2024). Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Models. arXiv preprint arXiv:2404.11457.

2. The experimental description is not convincing enough in demonstrating that "RAG leads to unfairness in LLMs," as it lacks the necessary results, i.e., the introduction of RAG in Fig. 3 not only leads to a decrease in fairness, but also a decrease in accuracy, which means that the data quality is lower. Does it mean that the degradation of fairness comes from the data itself rather than RAG?  along with other experimental weakness listed in the Questions
3. In the discussion section, the authors aim to analyze existing strategies cannot mitigate the bias arising from RAG. However, they use an unfairness rate of 1.0. I believe it would be more convincing to use an unfairness rate of 0.0, as this would better support the authors' argument that “retrieval-augmented generation undermines fairness” rather than suggesting that “low-quality data undermines fairness.”
4. Lack of some analysis on how to mitigate the author's claim unfairness issue from RAG effectively

Meta-review: This paper investigates fairness risks introduced by Retrieval-Augmented Generation (RAG) in LLMs under three levels of dataset censorship (uncensored, partially censored, and fully censored), demonstrating that RAG can exacerbate biases even in bias-censored datasets. While the study addresses an important and timely topic with a robust experimental design across multiple tasks, it suffers from limited novelty, failing to differentiate itself from prior work. The analysis is unconvincing as fairness degradation coincides with accuracy declines, suggesting issues may stem from data quality rather than RAG itself. Additionally, ambiguous bias classifications, unclear dataset definitions, missing statistical tests, and a lack of qualitative examples weaken the paper's contributions. Without more rigorous and deeper analysis, the paper falls short of providing actionable insights, and I recommend rejection.

Decision: Reject