FairImagen: Post-Processing for Bias Mitigation in Text-to-Image Models
Authors: Zihao Fu
Ryan Brown
Shun Shao
Kai Rawal
Eoin D. Delaney
Chris Russell
Venue: NeurIPS 2025 poster ()
Keywords: Fairness
Generative Fairness
Image Generation
Stable Diffusion
Primary area: social_and_economic_aspects_of_machine_learning
TL;DR: We propose FairImagen that debiases text-to-image models by post-processing prompt embeddings, improving fairness across gender and race without retraining the model.

Abstract: Text-to-image diffusion models, such as Stable Diffusion, have demonstrated remarkable capabilities in generating high-quality and diverse images from natural language prompts. However, recent studies reveal that these models often replicate and amplify societal biases, particularly along demographic attributes like gender and race. In this paper, we introduce FairImagen (https://github.com/fuzihaofzh/FairImagen), a post-hoc debiasing framework that operates on prompt embeddings to mitigate such biases without retraining or modifying the underlying diffusion model. Our method integrates Fair Principal Component Analysis to project CLIP-based input embeddings into a subspace that minimizes group-specific information while preserving semantic content. We further enhance debiasing effectiveness through empirical noise injection and propose a unified cross-demographic projection method that enables simultaneous debiasing across multiple demographic attributes. Extensive experiments across gender, race, and intersectional settings demonstrate that FairImagen significantly improves fairness with a moderate trade-off in image quality and prompt fidelity. Our framework outperforms existing post-hoc methods and offers a simple, scalable, and model-agnostic solution for equitable text-to-image generation.

Fairlmagen: Post-Processing for Bias Mitigation in Text-to-Image Models
Zihao Fu
Ryan Brown
Shun Shao
The Chinese University of Hong Kong zihaofu@cuhk. edu.hk hk
University of Oxford
University of Cambridge ss30470cam. ac. uk
blac@9770ox.co ac uk
Kai Rawal
Eoin Delaney
Chris Russell
University of Oxford kaivalyarawal45@gmail com
Trinity College Dublin eoin. delaney@tcd. ie
University of Oxford chris. rusiellussellooi i. ox ac uk
Abstract
Text-to-image diffusion models, such as Stable Diffusion, have demonstrated remarkable capabilities generating high-quality and diverse images from natural language prompts. However, recent studies reveal that these models often replicate and amplify societal biases, particularly along demographic attributes like gender and race. In this paper, we introduce Fair MairImager] post-hoc debiasing framework that operates on prompt embeddings to mitigate such biases without retraining or modifying the underlying diffusion model. Our method integrates Fair Principal Component Analysis to project CLIP-based input embeddings into a subspace that minimizes group-specific information while preserving semantic content. We further enhance debiasing effectiveness through empirical noise injection and propose unified cross-demographi projection method that enables simultaneous debiasing across multiple demographic attributes. Extensive experiments across gender, race, and intersectional settings demonstrate that Fairlmagen significantly improves fairness with a moderate trade-off in image quality and prompt fidelity. Our framework outperforms existing post-hoc methods and offers a simple, scalable, and aodel--gnostic solution for equitable text-to- sot-tto-mmae generation.
Firefighter (Base) Gender Debias (FairImagen) Yoga Instructor (Base) Race Debias (FairImagen)
Figure 1: Fairlmagen mitigates demographic biases in text-to-image generation. Compared baseline Stable Diffusion (Base), our method (Fairlmagen) produces more balanced representations across demographic attributes such as gender and race, while preserving visual quality and semantic fidelity.
1
Introduction
Recent advances in text-to-image generation have led to the widespread adoption of models such as Stable Diffusion 21. DALL.E
4. Imagen [5), and Parti 6 These can produce photorealistic
http://githuhh.comm/urraaaaaaaai
39th Conference on Neural Information Processing Systems (NeurIPS 2025).

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and diverse images from natural language prompts. These models leverage powerful vision-language encoders such as CLIP 7 align textual and visual modalities. enabling open-ended image generation from arbitrary input. Due to their flexibility, scalability, and accessibility, these systems are increasingly integrated into applications across design, content creation, and interactive media [8 9].
However, studies have shown that these generative models often replicate and even amplify social biases present in the training data [I0-88. For example, prompts such as: "a photo of CEO" or "a nurse" typically yield images depicting white males and females, respectively, reflecting gender and racial stereotypes. These biases pose serious concerns regarding fairness, representation. and downstream harms, particularly as generative models are integrated into public-facing systems.
To mitigate such biases, researchers have proposed a variety of debiasing techniques. Methods fall into three main categories: Prompt-based, Fine-tuning, and Post-hoc editing (Table Prompt-based approaches [10 19 20 modify the input influence the model's output, but often require per image heuristic rewriting and manually-curated prompts. Fine-tuning methods [21) (2) retrain or adapt parts of the model to encode fairness objectives. but they are computationally intensive and require access to model internals. Post-hoc editing methods [23] 24 [3] modify prompt embeddings at inference without updating model weights, offering lightweight and deployment-frieee ffrennnyy alternative. Each category exhibits differing trade-offs in terms of fidelity, interpretability, and generalizability.
We focus on post-hoc editing methods due to their simplicity and compatibility with wide range of off-the-shelf diffusion models. Approaches such as SDID (3 and TBIE (2 demonstrated the feasibility of manipulating prompt embeddings to mitigate demographic bias. SDID identified a gender direction by subtracting CLIP embeddings of pairs of hand-crafted prompts; adding or subtracting this vector in generation. This heavily relies the group bias being linearly separable and correctable via modifying embeddings in single direction. This is inappropriate for debiasing involving more than two demographic groups, e.g., ethnicity. TBIE improves over SDID by applying PCA on CLIP embeddings of gender-related words to identify bias directions in a data-driven way, However, PCA is performed on simple gendered words without an explicit optimization criterion for content aligmment. As a result, the debiasing process is often overly aggressive, removing not just demographic cues but also key semantic information. This results in semantic drift, loss of prompt fidelity, and unnatural image generation. Both methods offer limited control over the trade-off between fairness and fidelity, and neither generalizes well to intersectional prompts or unseen groups.
To overcome these limitations, we propose a novel post-hoc debiasing framework we call FairImagen (Fair Image Generation). It explicitly integrates Fair Principal Component Analysis (FairPCA) (25) into the Stable Diffusion pipeline and optimizes for semantic preservation while minimizing group- dependent variance. Fairlmagen operates in three stages: first, it extracts CLIP-based prompt embeddings; second, it applies fairness-aware projection using FairPCA to remove group-dependent directions from both pooled and token-level embeddings: finally. it synthesizes images from the transformed embeddings using a modified Stable Diffusion decoder. To further enhance performance, we incorporate an empirical noise injection scheme to avoid overly neutralized outputs, and propose a unified dess-reemoga debiasing formulation to jointly mitigate intersectional bias. Unlike existing post-hoc approaches, Fairlmagen offers precise control over the trade-off between fairness and content alignment. It is fully compatible with off-the-shelf diffusion models, supports multiple demographic attributes simultaneously, and preserves visual quality while effectively reducing bias.
Our contributions are as follows: (1) We introduce a post-hoe fairness framework that integrates FairPCA with diffusion-based text-to-image generation, enabling bias mitigation without model retraining. (2) We propose empirical noise injection to obscure residual demographic signals and improve fairness-pere perroosaape trade-offs. (3) We develop a cross-demographic debiasing formulation that handles multiple protected attributes in a unified manor, avoiding over-pruming from sequential projections. (4) We conduct extensive quantitative and qualitative evaluations across gender, race, and joint debiasing tasks, demonstrating that our method outperforms existing post-hoc baselines.
2 Related Works
Existing debiasing methods for text-to-image generation can be categorized into three types: Prompt- based,
and Training-fres free methods. As summarized in Table no single category universally superior; the choice of method often depends on the specific application scenario and deployment constraints.
2

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Table 1: Comparison of prompt-based, fine-tuning- based, and post-hoc editing methods for debiasing text- to-image generation.
Criteria
Prompt-based
Fine-tuning
Post-hoc editing
Training-fres free
Black-box compatible
Low human effort
X
Low computationa cost
Generalizable to new prompts
Strong hias mitigation
Preserves prompt fidelity
Easy deployment
X
Prompt-based methods mitigate bias by modifying the input prompts. Friedrich et al. 10 proposed Fair Diffusion using carrness-guided prompts constructed from demographic opposites. Sakurai and
Sato [9 utilized LLMs to automatically detect and revise biased prompts. Bansal et al.
20 and
Chuang et al. [26] examined ethical interventions and latent direction projection. Kim et al. 27 and
Al Sahili et al. [28] developed learned fairness prompts, and Bianchi al. [29] assessed the impact of
biased prompts at scale. These methods are flexible but often rely on heuristic external guidance
for every single image. This can be somewhat opaque and laborious [20 13..
Fine-tuning based methods update model parameters to enforce fairness, Li et al. [2] introduced Fair Mapping by training linear projection layer. Zhang et al. 13 aligned prompt embeddings with fair visual examples. Shen et al. [22 applied a distributional alignment loss for fairness. Kim et al.
[77. Orgad al. [30], and Gandikota al. [31] proposed fine-tuning specific modules or applying concept editing. Parihar et al.
[2) incorporated interpretable latent directions and population-level optimization, respectively. These methods provide effective bias mitigation but often require costly
model access and retraining.
Post-hoc editing methods avoid parameter updates and modify inference behavior. Zhang al. [13] and Li ct al. (23) manipulated prompt embeddings with CLIP-based or interpretable directions. Tanjim al. (24 used PCA to subtract biased components. Friedrich al. [IO] employed classifier- free guidance alternations. Sadat et al. 33 explored sampling noise perturbation and conditioning annealing reveal underrepresented concepts. Post-hoc filtering is also employed in some commercial systems (3. These methods are deployment-frieed frienddl,,l,, take advantage of both prompt- and model-based strategies, and avoid extensive retraining or heavy prompt engineering.
Method
We propose fairness-aware text-to-image generation framework, Fairlmagen. This framework integrates FairPCA (25) into Stable Diffusion 21. Our goal is to reduce social bias in image generation by modifying prompt embeddings prior synthesis, while preserving semantic fidelity.
To estimate and remove demographic information from prompt embeddings, we begin with a small training set of natural language prompts, each annotated with protected attributes such as gender or race (c.g., "a lady playing computer," "" "an Asian man holding a phone"). These prompts are used to construct FairPCA projection matrix that suppresses group-specific directions while retaining semantic content. The learned projection is then applied at inference time to unseen prompts, making Fairlmagen entirely training-free with respect to the diffusion model.
The framework consists of three main components: (1) Prompt Embedding Extractor, (2) Fair Representation Transformer, and (3) Image Generator. In addition, we propose an empirical noise injection scheme to prevent overly neutralized outputs and unified cross-demographic debiasing formulation to jointly mitigate intersectional bias.
3.1 Prompt Embedding Extraction
To construct the FairPCA projection, we begin with a small training set of natural language prompts (PI.... Pr), each annotated with a protected attribute label a; E A. These prompts are designed to be demographically informative yet semantically neutral (e.g., "a lady playing computer,"
Black man riding a bike"), and serve as the foundation for identifying group-dependent components in the embedding space.

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Given prompt E P, we first encode it using pre-trained CLIP model 77. Let (W1..... WT) be the tokenized prompt, where 7 is the number of tokens. The encoder outputs token-level embedding matrix Ep R where D is the embedding dimension, and a pooled embedding Ep E RDD The pooled embedding is computed as the mean of the token embeddings: Ép Σ Eplt]. These representations are extracted from the Stable Diffusion text encoder. Let = (Pi.... Pn} denote set of prompts, each associated with protected attribute labels a; A. For each attribute a, organize the pooled embeddings by group:
x= (Ep E RTXD, 7= (z:) {0. 1)nxC,
where zj is a one-hot group indicator for the attribute aj and G [9]. These grouped embeddings are used to estimate the bias direction and define fairness-aware projections.
3.2 Fair Representation Transformer
We use Principal Component Analysis (PCA) to approximate the original prompt embedding space with a lower-dimensional subspace that preserves semantic information. Specifically, we seek a projection matrix that can faithfully reconstruct the original embeddings from reduced set of basis directions.
Let P E R Dxd be projection matrix, where d < D. Classical PCA solves the following reconstructionn objective:
arg min
Σ | PPT x
=
arg max PERDsd P=l
Tr(P"XXTP),
(I)
PeRDsd.pT P=l
where x= =x x,, E RnXD the matrix of pooled prompt embeddings. The left-hand side minimizes the total squared reconstruction error of projecting the data onto a d-dimensional subspace, while the right-hand side expresses the equivalent trace maximization formulation.
To further ensure fairness, we also require that the projection removes demographic signals by aligning with the null space of group-dependent variation. Specifically, we define the group-dependent feature matrix B = Z' X RGx where Z E {0, 1 is the group indicator matrix. Each row of B captures the mean embedding direction for a specific demographic group. By constraining the projection matrix P to lie in the null space N(B), B), we ensure that the resulting representations are orthogonal any direction that separates groups in the embedding space, thereby eliminating linear demographic signals.
Building this intuition, FairPCA 25 incorporates fairness regularization term into the PCA objective, yielding the following formulation:
min - Txp A||
P P-1
where A is a hyperparameter controlling the trade-off between reconstruction quality and fairness. The first term ensures that the projection subspace remains a good approximation of the original feature space. The second term penalizes the degree to which the projected embeddings retain group-specific components, thereby reducing demographic separability. Once the projection matrix is obtained, we apply it during inference to both the pooled and token-level prompt embeddings:
F PP E.p. F = PPT.
3.3 Empirical Noise Injection
To further enhance diversity and realism, we introduce an empirical noise injection mechanism that perturbs representations along estimated group- depeddee directions. prevents the generated output from becoming overly neutral (c.g., generating man who appears feminine; see further discussion in Appendix E Let G denote the set of protected groups (c.g.. G (Male, Female)), and let E G be a particular group. For each group g, we compute its empirical bias direction
Σ Ep e
Ep F.
V/= =
[x(x

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where X(2) is the set of pooled embeddings belonging to group 8. and E is the overall mean embedding across all groups. We define an empirical distribution Dg as the set of scalar projections of group-specific embeddings onto the bias direction:
Dg [v E E x(s)).
Each value E Dg represents the magnitude of projection of an embedding onto the bias direction Yg. quantifying how strongly that embedding aligns with group-specific attributes. To inject noise in the inference stage, we sample 8 Dg and apply the perturbation:
E = E + - Vg-
where e is a tunable noise scale parameter. This procedure introduces controlled variability along biased directions to obscure protected group information while preserving semantic structure. We also conduct experiments with additional noise injection strategies, including mrean-haed,, Gaussian, eieeddiiectional, and deterministic shift variants. A detailed comparison of these methods and their impact on fairness and image quality is provided in Appendix D Among these strategies, empirical noise demonstrates the best overall performance.
3.4
Cross-Demographics Debiasing
The FairPCA framework 25 debiases multiple demographies by jointly encoding them into multi-dimensional attribute matrix. minimizes group-specific information across attributes simultaneously via a single projection derived from a stacked group indicator matrix. However, when applied image generation, this approach fails adequately represent all demographics, forces features to be orthogonal to each group direction. Consequently, the model tends to preserve information aligned with only one group at time, resulting in degraded contextual fidelity and the loss of important visual details in the generated images.
To overcome this, we propose a unified cross demographii debiasing method that constructs single attribute space based on the Cartesian product of all group combinations. For example, if the gender attribute has two groups (Male, Female) and the race attribute has three groups (White, Asian, Black), we define a joint attribute space with six composite groups: Ajoint (White Male, White Female, Asian Male, Asian Female, Black Male, Black Female). We then apply Fair Representation Transformer once over this joint attribute space. Therefore, our cross- gross-demographic debiasing approach can debias all demographies simultaneously. We have also conducted experiments with alternative strategies for handling multiple demographic attributes, including stacking and sequential projection. For a comprehensive comparison of these cross-demographic debiasing methods, please refer to Appendix G
3.5 Image Generator
After debiasing, we pass the transformed embeddings into a customized Stable Diffusion pipeline 21 which supports external prompt embeddings. Specifically, generate the image Ip =G(E", where G(-) denotes the generation function, and En and E are the pooled and token-level debiased embeddings.
4 Experiments
4.1
Dataset
The Winobias dataset consists of 46 professions, collected from the US Bureau of Labor Statistics, that
35) our experiments, we extend this list to 120 professions using publicly available list Our list covers professions that
are stereotypically considered "male biased" or "female biased" [31 30
have been found to be biased towards men (c.g., Janitor or CEO) and women (c.g., Nurse or Librarian)
in generative AI systems (36).
2 The full list is included in our supplementary material with the code. We manu- ally extended the winobias list using publicly available list of occupations from Wikipedia: https://en
htt.://wwwwiiiiiaa...em wtwpeediia.og//iiiiitssttte of occupations
5

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4.2 Experimental Settings
Our modified pipeline extends HuggingFace's StableDiifusioon3PPpelee to accept external embeddings and apply Fairlmgen at inference time. We generate images using classifier-free free guidance with scale w w= 7.0 and T = 28 diffusion steps. Images are generated in batches (12 per prompt), stitched, and evaluated with fairness and perceptual quality metries. We split the dataset into development set of 20 samples and use the remaining 100 samples as the test set. We tune all models on the development to maximize the average (AVG) score and report their performance the test set. We run all the models on a NVIDIA A100 GPU with 80 GB memory.
4.3 Evaluation
We report four scalar metrics: Fairness, Aceuracy, MUSIQ, and their average.
Fairness is monitored using a lightweight pretrained facial attribute classifier from DeepFace [37] This classifier detects and counts members of each group in every generated image. distribution of counts is scored with
Next the
Σ, pp: - -1
2(1 - /
following the normalized -eviiiatoooiiiiiionn formulation of Teo et al. 138. Here is a vector of group proportions and is the number of groups. A score of I indicates that all groups are generated at the same rate, while a score of 0 indicates that only one group is generated (c.g., all men when considering gender the protected attribute and prompted to generate images of CEO). We measure cunder-based groups of male and female individuals alongside ethnicity-basee groups of asian, black, latino hispanic, middle eastern, and white individuals. We also consider intersectional groups of gender and ethnicity throughout the paper.
Accuracy is measured using CLIPScore [39, which quantifies how closely generated image matches its text prompt. Specifically, we compute the cosine similarity between the prompt emhedding and the image embedding produced by CLIP (ViT-B/16 backbone). Following best practices reported by Hessel el al. [39], we multiply the CLIPScore values by 2.5 for scaling as the original scores typically fall between 0 and 0.4.
To assess the visual quality of an image, we use MUSIQ 40 which a no-reference perceptual- quality model trained millions of aesthetic ratings. This metric selected due to its flexibility, as it can work with native resolution images with varying sizes and aspect ratios.
4.4 Comparison Models
Base is the vanilla Stable Diffusion model, which directly generates images from the prompt without any fairness intervention.
FairPrompt follows 10 19 by using human-designed prompts for each image. We evenly apply different prompts corresponding to protected groups for each individual image. This serves as an upper-bound performance baseline, as each prompt is specifically tailored for fairness.
ForcePrompt explicitly includes fairness-relate related instructions (e.g. "Please avoid gender bias.") in the prompt, directing the Stable Diffusion model to generate fair representations.
SAL 41 uses Singular Value Decomposition to project the input representations into directions with reduced covariance with the biases.
CDA (Counterfactual Data Augmentation) 43 44 replaces gendered words with their counterfactual counterparts, such as replacing "man" with "woman." We follow the CDA methodology construct counterfactual samples and augment the dataset.
TBIE (Text-Based Image Editing) 24 applies PCA to gender-related words and performs debiasing along the identified principal components.
SDID
Interpretable Diffusson) 23 computes gender vector using the difference between gender-specific and gender-neutral embeddings, and injects this vector into the prompt embedding.

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Taimess
Gender
Accomcy
MUSIQ
Ang
Famess
Accuracy MUSIQ Ang
Gender
Fximess
Accomay
MUSIQ Ang
Mase
0.187
0.785
0,574
0,589
Base
0.195
0.785
0.574
0,517
Bore
0.363
0.193
0.785
0,574
0,508
FaiPhounp
0.732
0.766
0.586
0.665
FaiPPoungt
0.444
0.252
0.566
0.587
RaiProope
0.69
0:475
0.747
0.574
0.671
PonesProupt
0.292
0.755
0.501
0,549
FreceProrap
0.266
0.761
0.574
0.534
FonePPoongt
0.267
0.384
0.764
0.59
0.547
SAL
0.277
0.279
0,602
0,533
SAL
0.262
0.788
0.607
0.551
SAL
0J182
0214
0.776
0.59
0,519
CDA
0.547
0,772
0,549
0,625
CDA
0.358
0.772
0.557
0.556
CIM
0,302
0.27
0.779
0.557
0.566
TRIE
0.35
0.7R3
0.567
0.566
TRIE
0.566
0.262
0.532
0.553
TRIE
0:40
0.266
0.776
0,546
0.574
SDID
0.507
0.276
0.553
0.612
SDID
0.57
0.27
0.537
0.559
SDID
0.223
0.256
0.782
0.556
0.5
SDID-AVG
0.315
0.783
0.562
0.553
SDID-AWG
0.36]
0.769)
0.544
0.558
SDID-AVG
0.352
0.28
0.778
0.553
0.561
ITI
0.27
0.769
0,525
0,522
ITI
0.214
0.27
0.53
0.504
0.32
0.233
0.747
0,467
0.11
PairQuee
01197
0.800
0.21
0.542
FairQucu:
0.118
0.776
0.611
0.495
FarQuarc
0.0667
0:34
0.771
0.56
0.47
Farlhagen
03:56
0.771
0.54]
0.624
Fardiages
0.589
0.76
0.536
0.562
Farloggen
0.537
0.32
0.783
0.544
0611
FürlnugeutT5
0.572
0.764
0,533
0.624
Fardnages-T3
0.586
0.76
0.537
0.561
RallugnnTT 0.48
0.31
0.766
0,532
0.913
FürdnggenOCC
0.573
0.767
0.534
0,625
FarlinggesOCC
0.587
0.76
0.536
0.561
Fadlmggon- OC
0.482
0.311
0.766
0.532
0.993
(a) Gender Debias.
(b) Race Debias.
(c) Gender Race Debias.
Table 2: Quantitative evaluation results for debiasing text-to-image generation across three settings:
(a) gender, (b) race, and (c) both gender and race. Fairlmagen achieves the best overall performance among post-hoc methods, striking strong balance between fairness, accuracy, and perceptual quality.
SDID-AVG extends the SDID 23 model by computing neutral embeddings through averaging the embeddings within each protected group.
ITI-GEN 3
extracts gender-related CLIP embeddings from images and adds them to the prompt embeddings prior to image generation.
rethinks prompt learning approaches by identifying abnormalities in early denoising steps. It proposes Prompt Queuing (using base prompts without sensitive attribute tokens in initial steps) and Attention Amplification (enhancing attribute representation in later steps) to modify cross-attention maps during generation, achieving competitive fairness while improving image quality.
FairQueue 45
FairImagen-T5/000 To evaluate if our method is compatible with encoders beyond CLIP, we evaluate using alternative text encoders. FairImagen-TS replaces the CLIP text encoder with T5 [46), while Fairlmagen OC uses OpenCLIP (47) instead of the original CLIP encoder. These variants demonstrate the generalizability of our fairness framework across different text encoding architectures.
4.5
Experimental Results
4.5.1 Main Experiments. We apply our model, as well as other baseline models, to generation tasks involving debiasing with respect to gender ($4.5.1 ,. race ($4.5.1) and both gender and race simultaneously ($ The results show that: (1) our proposed Fairlmagen model outperforms all postprocessing baseline models in terms of fairness scores across all three scenarios, demonstrating its effectiveness in mitigating bias in various contexts; (2) our proposed Fairlmagen model also outperforms all postprocessing models in terms of average (AVG) scores, indicating that it achieves the best balance among fairness, accuracy, and image quality; and (3) our model consistently outperforms all postprocessing baselines when debiasing both gender and race simultaneously. highlighting its strong capability in addressing slightly lags behind other models in terms of Accuracy and MUSIQ. However, given the substantial improvement in Fairness, this trade-off is justified and considered worthwhile. (5) FairPrompl achieves the best performance across all experiments. on manually designed prompts tailored to each individual image, which is both time-consuming and aabor-intensive As such, it serves primarily as an upper bound to illustrate the best possible performance model can achieve on this task.
attribttt bias mitigation. (4) Our proposed Fairlmagen model
should be noted, however, that this model relies
4.5.2 Effect of Hidden Dimension on Fairlmagen Performance. To investigate the impact of dimensionality reduction on the effectiveness of Fairlmagen, we vary the number of retained principal components (i.e., hidden dimensions) from 200 to 2000. Figure 2 shows performance trends across three scenarios: (a) gender debiasing, (b) race debiasing, and (c) joint gender and race debiasing. The results demonstrate a clear trade-off between fairness and other metrics as dimensionality varies. Notably, reducing the number of components tends to improve fairness scores, particularly in gender and race separately, but at the cost of reduced Accuracy and MUSIQ. In contrast, larger hidden dimensions preserve visual and semantic fidelity better, but may reintroduce bias. The joint debiasing setting (Figure [ further reveals the challenge of balancing fairness across multiple attributes simultaneously, with Fairness metrics for gender and race sometimes diverging. Overall, these
7

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0.
Pi 1L5s
Ag
0.7
0.5
0.0
9.2
9.4
0.2
(a) Gender Debiasing
(b) Race Debiasing
(c) Gender Race Debiasing
Figure 2: Effect of hidden dimension size Fairlmagen performance across different debiasing
settings: (a) gender debiasing, (b) race debiasing, and (c) joint gender and race debiasing. Reducing number of retained dimensions improves fairness but may reduce Accuracy and MUSIQ, highlighting the trade-off between fairness and semantic or visual fidelity.
0.8
0.5
0.5
0.8
0.4
0.7
9.3
Bny
0.4
0.2
(a) Gender Debiasing
(b) Race Debiasing
(c) Gender Race Debiasing
Figure 3: Effect of e-noise on Fairlmagen performance across different debiasing settings: (a) gender debiasing, (b) race debiasing, and (c) joint gender and race debiasing. Increasing e-noise enhances fairness, especially in the joint setting, but introduces a moderate drop in Accuracy and MUSIQ. This highlights the need to balance fairness improvements with fidelity preservation when tuning noise.
results indicate that non-linear biases are more likely to occur in higher- sigeer-dimensional spaces, and points towards the importance of selecting an appropriate dimensionality to achieve desirable fairness-fidelity trade-off.
4.5.3 Effect of Empirical Noise on Fairlmagee Debiasing. We further examine the influence of the empirical noise parameter (e-noise) in the Fairlmagen framework, which controls the magnitude of perturbation added to simulate empirical distributions aligned with different protected attributes. As e-noise increases, the sampled directions more closely follow gender- or race-specific variations, enabling stronger debiasing effects. Figure 3
presents the performance across gender, race, and joint gender+race debiasing tasks under varying noise levels from 0.0 to 1.0. Higher e-noise values significantly improve fairness metrics, particularly in the joint debiasing setting, where both gender and race fairness steadily increase. However, this comes with a modest trade-off in Accuracy and MUSIQ, suggesting balance must be struck between fairness gains and fidelity preservation. These results validate the effectiveness of controlled empirical noise injection in sampling representative latent directions that better approximate the real distribution of protected attributes.
4.5.4 Qualitative Case Study. To qualitatively assess our debiasing strategies, we examine the prompt "Generate photo of face of a CEO" under four Fairlmagen settings: Base, Fairlmagen- Gender, Fairlmagen-Race, and hewtiiaggenn-nnnee--eeeeeee predominantly generates white male faces, reflecting societal bias. Fairlmagen-Genner (Figure 4p) increases female representation while maintaining visual coherence. Fairlmagen-Raee (Figure 4 introduces more racial diversity, including Black and Asian CEOs. Combining both dimensions (Figure (4)) yields broader diversity but also introduces visual inconsistencies in background and style, due stronger empirical noise. This also highlights the trade-off: enhancing fairness can compromise visual stability and prompt fidelity.
As shown in Figure 4 the Base model
4.5.5 Evaluation on Occupations with Man/White Dominance. To evalluaee debiasing performance, we analyze occupation prompts that exhibit strong male and white biases in the baseline model. Figure 5 shows that the Base model produces predominantly male outputs, while Fairlmagen and FairPrompt significantly improve gender balance. Similarly, o strong white dominance

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(a) Base
(b) Pairlmagen-Gente
(c) Fairlmagen- Race
(d) Fairlmagen-
Figure 4: Generated results for the prompt "Generate a photo of face of CEO" under four Fairlmagen settings: (a) Base (no debiasing). (b) Säirlaggen-Gender (c) Fairlmagen-Race and (d) Seltimaagen--nnne---eeee
Debiasing increases demographic diversity across gender and race dimensions. However, stronger crrtaainng-sspeciin under intersectional settings introduce variation in background and style, reflecting trade-off between fairness and visual consistency.
Cender Propert Matred Dyckrad Nar Ratol
Pender Iroperiant Mechad Sottedll
1.0
5.4
Figure 5: Gender proportions
Figure 6: Race proportions
Figure 7: Gender distribution for prompts with historically male- associated roles. While most methods retain male-dominant outputs, FairPrompt introduces more females, contradicting his- torical facts. In contrast, Fair- Imagen preserves the intended gender associations.
for male-dominated occupations. Each bar shows the proportion of
for white-dominated occupa- tions. Each bar shows the propor- tion of racial groups in the gener- ated outputs, sorted by white ra- tio. Fairlmagen and FairPrompt noticeably reduce white overrep- resentation and enhance racial diversity.
male and female outputs gener- ated by different methods, sorted by male ratio. Fairlmagen and FairPrompt substantially reduce male overrepresentation com- pared to baselines.
in the Base model, with Fairlmagen and FairPrompt increasing representation of Black, Asian, and Latino Hispanic individuals. These results demonstrate that Fairlmagen effectively mitigates demographic bias in skewed prompts, achieving fairness comparable to FairPrompt while remaining model-agnostic and training-free
4.5.6 Robustness to Demographically Determined Prompts. A major challenge in farress--waan generative modeling is to ensure that debiasing methods do not compromise semantic fidelity, particularly when prompts inherently reflect justified demographic attributes. In real-world use cases, certain prompts-such as those referencing historical figures or culturally specific roles-are expected yield outputs with a specific gender association. Overcorrecting in such cases may lead to semantically incongruent or historically inaccurate generations, undermining user trust and model reliability Therefore, it is essential for fairness interventions to be context-aware and capable of preserving prompt intent when the bias is grounded in legitimate semantics. To this end, we evaluate whether Fairlmagen can maintain semantic alignment when prompts exhibit strongly determined gender associations. We focus on examples such as "a middle ages blacksmith", "the Pope", and "the King of France", which traditionally imply male representations. Figure shows that across these historically gender-fixed prompts, most models continue to generate predominantly male outputs. Notably, Fairrrompt increases the proportion female representations, even male-dominant
3 httpge/ttgggg...a.a..m.../oooood
german uni forms.
and http:///wwweeeeg....///////////m wwwpeerrrttt-iiinccaaattteeee historicca

---

(a) Base
(b) FairPrompt
(c) Fairlmagen
Figure 8: Generated results for the prompt "a middle ages blacksmith" using three different methods: (a) Base, (b) FairPrompt, and (c) Fairlmagen. FairPrompt introduces female representations even when the prompt implies a male role, while Fairlmagen preserves the intended gender semantics, yielding historically aligned outputs.
contexts. Figure 8 presents qualitative comparisons of images generated for the blacksmith prompt using the Base, FairPrompt, and Fairlmagen models. While FairPrompt introduces female depictions regardless of the prompt's semantics, Fairlmagen respects the gender bias encoded in the original embedding and yields predominantly male outputs. This demonstrates a key strength of Fairlmagen: when a prompt conveys strong and contextually justified gender preference, Fairlmagen does not override it unnecessarily. As such, Fairlmagen adapts to prompt intent while still being effective in mitigating bias in less explicitly gendered scenarios.
5
Conclusion
We present Fairlmagen, a novel post-hoc debiasing framework for text-to-ine text-to-image generation that integrates FairPCA into the Stable Diffusion pipeline. Our method modifies prompt embeddings to mitigate demographic biases without requiring model retraining prompt rewriting. Through fairness-aware projection, empirical noise injection, and unified cross-demographic formulation, Fairlmagen achieves strong bias reduction results while preserving visual fidelity and prompt alignment. Extensive experiments across gender, race, and intersectional attributes demonstrate that approach outperforms existing post-hoc basclines on both fairness and utility metrics. By offering a training-free model-agnostie and extensible solution, Fairlmagen paves the way for more equitable and controllable generative systems.
Acknowledgements
This work was supported through research funding provided by an EPSRC Doctoral Scholarship. the Wellcome Trust (grant no. 223765/2/21/2)) Sloan Foundation (grant no. G-2021-16779), Department of Health and Social Care, EPSRC (grant no. EP/Y019393/1), Origin Investments, and Luminate Group.
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Appendix.
Supplementary Material
Impact Statement
This work contributes to the development of fair and inclusive generative AI systems by introducing Fairlmagen, training-freee and model-agnostic framework for mitigating demographic bias in text-to-image diffusion models. Fairlmagen offers a practical and scalable solution for enhancing fairness in image generation without requiring access model internals manual prompt design. Its ability to jointly address multiple demographic attributes while preserving visual fidelity makes particularly well-suited for deployment in weal-world applications such as digital media, design, and educational content. By reducing the social harms associated with biased generation and enabling more representative outputs, Fairlmagen supports the broader goal of responsible and equitable Al deployment.
B Ethical Statement
This research aims to address ethical concerns surrounding bias and representation in text-to-image generative models. Our proposed framework, Fairlmagen, is designed reduce the amplification of demographic stereotypes without compromising image quality or user intent. We acknowledge that fairness multi-faceted and context-dependent concept, and our method focuses primarily on gender and racial representation, which may not capture the full spectrum of social identities or cultural nuances.
We do not collect any personal or sensitive user data our experiments. All generated images produced from synthetic prompts, and demographic groupings based commonly used protected attributes fairness research. While Fairlmagen mitigates certain biases, we caution against interpreting it complete solution to fairness in generative models. Ongoing monitoring, inclusive evaluation, and engagement with affected communities remain essential for ensuring responsible deployment.
Our code and findings will be released research community to promote transparency and further development of fair and accountable generative AI.
C Limitations
Despite the strengths of Fairlmagen as post-hhor hoc, training- free debiasing framework, several limitations remain. First, the method currently focuses on a limited set of protected attributes primmarily binary gender and coarse categorization of race. As frequently noted [48], such simplifications may overlook more nuanced or intersectional demographic identities, such as non-binary gender expressions or multi-ethnic backgrounds. Second, as Fairlmagen operates CLIP-based based prompt embeddings, inherits any intrinsic biases present in the CLIP encoder, which itself is trained on large-scale web data with limited curation. While FairPCA reduces group-dependent variance, it cannot fully disentangle bias that is deeply entangled with semantic meaning. Third, although empirical noise injection and projection dimensionality offer tunable fairness-utility trade-offs, determining the optimal balance often requires empirical tuning and may vary across tasks. Additionally, while the framework performs robustly across wide range of prompts, effectiveness may degrade for prompts that are strongly tied to cultural or historical contexts, where bias removal risks semantic distortion. Lastly, our evaluation focuses on specific benchmark of occupational prompts: broader testing across domains, cultures, and creative settings is needed to fully validate generalizability and uncover edge cases where the method may fail.
D Effeet of Noise Injection Variants
To investigate the role of noise in fairness-aware generation, we compare several noise injection schemes within the Fairlmagen framework. The Empirical Noise method (enoise) samples both direction and magnitude from real group-specific embedding distributions, introducing realistic and data-driven perturbations. Mean Empirical Noise uses the average projection magnitude instead
14

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Gender Fairness
Accuracy
MUSIQ
Avg
Empirical Noise
0.455
0.808
0.567
0.61
Mean Empirical Noise
0.432
0.788
0.472
0.364
Fixed Directional Noise
0.258
0.76
0.542
0.52
Fixed Directional Noise (b=1)
0.197
0,761
0.547
0,502
Random Gaussian
Fixed Random Gaussian Noise
0,258
0,763
0.538
0.52
Constant Bius Shifl
0.242
0.757
0.541
0.514
Rypass Projection
0.136
0.817
0.631
0.528
Table 3: Comparison of different noise injection strategies used in the Fairlmagen framework for gender debiasing. Each method perturbs prompt embeddings in distinct ways to obscure demographic signals. The results highlight the trade-off between fairness, semantic accuracy, and visual quality across noise types.
of sampling, resulting in more stable but less diverse shifts. Fixed Directional Noise adds binary noise (+1) along the hias direction and optionally biases the sign, simulating controlled reversals in group representation. Random Gaussian Noise injects direction-agnostic perturbations, using Dirichlet-wiighted average when multiple groups are involved. Its variant, Fixed Random Gaussian Noise, reuses a fixed noise vector for consistency. Constant Bias Shift applies deterministic translation in the bias direction to all embeddings, representing a non-random intervention. Lastly, Bypass Projection disables the FairPCA projection and uses the original prompt embedding, serving as an ablation to isolate the effect of projection from noise injection.
The results in Table 3 show that noise type substantially influences the fairness-utilit trade-off. Empirical Noise performs best overall, balancing improved fairness with minimal degradation in image quality and semantic alignment. Mean-based perturbations offer more stable behavior but slightly compromise fairness effectiveness. In contrast, direction-agnostic and fixed-noise variants underperform due their limited alignment with demographic structures. Deterministic shifting introduces consistent but ineffective debiasing, and bypassing the projection leads to high image quality but minimal fairness improvement. These findings underscore the importance of designing context-aware, group-sensitive noise to support effective and reliable debiasing in post-hoc settings.
E
Qualitative Impact of Empirical Noise
To further understand the role of empirical noise in enhancing fairness without overly neutralizing semantic attributes, we conduct qualitative case study on firefighter generation. In particular, we compare two male firefighter images produced by Fairlmagen under different noise configurations. Both images are generated from the same prompt-"Generate "Generate face image of a firefighter" but differ in whether empirical noise is applied.
As shown in Figure 9 the image generated with empirical noise injection depicts aligning with natural variations in male appearance. In contrast, the image generated without empirical noise produces clean-shaven face, which appears overly neutral and lacks realistic diversity. This comparison highlights that without empirical perturbation, Fairlmagen may suppress group-dependent cues aggressively, leading to sanitized outputs that obscure important intra-group variation. Empirical noise mitigates this effect by reintroducing sampled group characteristics, such as facial hair, thereby preserving authenticity while maintaining demographic balance. This illustrates the importance of using noise not merely as randomness, but as mechanism to approximate the true variability within protected groups
with beard,
F
Effect of Empirical Noise Magnitude
To better understand how empirical noise magnitude influences demographic representation, we conduct a controlled experiment in which we vary the strength of empirical noise injection in Fairlmagen from -5 to 15. In this setting, empirical noise applied by perturbing the prompt embedding along the learned demographic bias direction, where the scalar magnitude modulates how strongly group-specific features (e.g., gender-related appearance) are emphasized. We generate firefighter images at each noise level and compute the gender proportions in the outputs.
15

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(a) With Empirical Noise
(b) Without Empirical Noise
Figure 9: Visual comparison of firefighter images generated by Fairlmagen. (a) With empirical noise: preserves natural male features like a beard. (b) Without noise: produces an overly neutral, clean-shaven appearance.
Woman
Progortion
0.0
Figure 10: Effect of empirical noise magnitude gender proportions in firefighter generation. Negative values shift the embedding toward the male-associated direction, while positive values favor female features. The curve is not centered due to male-dominance in the original training prompts.
As visualized in Figure 10, increasing the empirical noise magnitude leads to clear transition in output gender. Negative values (corresponding to movement in the male-associated direction) produce predominantly male faces, while large positive values favor female representations Around zero, the output becomes more balanced. However, the inflection point where the transition occurs is not perfectly centered at zero; instead, the gender ratio begins to shift significantly only slightly positive values. This asymmetry arises because the original training prompts used to construct the FairPCA projection are themselves male-dominated, which results in a skewed embedding space where zero noise still retains residual male characteristics. Consequently, stronger positive perturbations are needed to counteract this bias and achieve gender balance.
This finding reinforces the notion that the empirical bias direction learned by Fairlmagen captures semantically meaningful demographic information. Moreover, it illustrates how noise magnitude can be used as controllable parameter modulate output demographics, with meaningful behavior emerging even from simple linear perturbations.
16

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Gender Fairness
Race Fairness
Accuracy
MUSIQ
Avg
Base
0.0758
0.136
0.819
0.616
0.504
FairPrompt
0.652
0,482
0,778
0.60
0.676
Fairlmagen (Stack)
0.227
0.345
0.799
0.569
Fairimagen (Sequential)
0.106
0,345
0,771
0.525
0.467
Fairlmagen (Croes)
0.429
0.792
Table Comparison of cross-demoraaphic debiasing methods under joint gender and race settings. Fairlmagen (Cross) achieves the best fairness-utility trade-off among post-hoc methods.
G Comparison of Cross-Demographic Debiasing Strategies
To evaluate the effectiveness of different strategies for handling multiple protected attributes, we compare three roos-demoggaphic debiasing approaches within the Fairlmagen framework: Stack, Sequential, and our proposed Cross method. The results are presented in Table
The Stack method, originally proposed in the FairPCA paper [25]. constructs a single fairness projection by stacking group indicator vectors from all demographic attributes (e.g.. gender and race) into a combined group matrix. While this approach is simple and computationally efficient, it tends to over-represent groups with stronger bias signals, leading to suboptimal fairness across multiple dimensions.
The Sequential method applies FairPCA in multiple stages, projecting out bias directions for each protected attribute one after another. While this offers a conceptually modular way to remove group-specific information, it suffers from compounded information loss: each projection removes components orthogonal to prior ones, making it difficult to preserve semantic fidelity across multiple passes.
In contrast, our Cross method explicitly constructs a joint demographic space based on the Cartesian product of all attribute groups (e.g., White Male, Asian Female), and learns unified projection that removes shared group-dependent directions single step. This ensures that the projection is optimized for intersectional fairness without over-pruning or repeated reconstruction loss.
As shown in Table 4 the Cross method achieves the best balance between gender and race fairness, while also maintaining competitive Accuracy and MUSIQ scores. Compared to Stack and Sequential variants, Cross substantially improves fairness without significantly compromising generation quality, highlighting its effectiveness in multi-attribute debiasing scenarios.
H Age Debiasing Experiments
To demonstrate the generalizability of Fairlmagen beyond binary gender and race categories, extend our evaluation to age debiasing. Age represents a particularly challenging demographic attribute due to the inherent difficulty in distinguishing precise ages (e.g., differentiating between 35 and 38 years old in generated images). Therefore, we categorize age into three distinct groups: young (approximately 18-30 years), middle-aged (approximately 31-55 years), and elderly (approximately 56+ years).
The age debiasing task follows the same experimental setup as our gender and race experiments, using the same set of occupational prompts and evaluation metrics. We apply our FairPCA- projection to remove age-correlated variance from prompt embeddings and use empirical noise injection to encourage diverse age representations in generated images.
As shown in Table 5, the results demonstrate that: (1) Fairlmagen and its variants (FairImagen- T5, Fairlmagen-OC) achieve the highest fairness scores among all compared methods, confirming the effectiveness of our approach in age debiasing: (2) Fairlmagen- -OC achieves the best overall performance among our proposed variants, indicating strong compatibility with alternative text encoders: (3) all Fairlmagen variants maintain competitive accuracy and visual quality metrics with only modest degradation compared to the baseline, preserving the generation quality while improving fairness; and (4) the average scores show that Fairlmagen variants achieve the best overall balance between fairness and utility, validating the effectiveness of our post-hoe debiasing framework across different demographic dimensions beyond binary gender and race categories.
17

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Fairness
Accuracy
MUSIQ
Avg
Base
0.165
0.783
0.572
0.507
FairPrompt
0.21
0.764
0.582
0.519
ForcePrompt
0.202
0.765
0.515
SAL
0,193
0.785
0.595
0.524
TBIE
0.403
0.748
0.557
0.569
SDID
0,253
0.779
0.545
0.526
SDID-AVG
0.368
0.75
0.548
0.556
ITI
0.17
0.772
0.525
0.489
FairQueue
Fairlmagen
0.412
0.742
0.558
0.57
FairImagen-TS
Fairlmagen-OC
0.45
0.738
0.563
0.584
Table 5: Age debiasing results comparing Fairlmagen variants against baseline methods. Ages are categorized into young, middle-aged, and elderly groups. Fairlmagen variants achieve the highest fairness scores while maintaining competitive quality metrics.
Fairness(
Accuracy(SS)
MUSIQ(()
Avg("))
Base
0.61
0.05
0.39
FairPrumpt
1.85
0.14
0.46
0.66
ForcePrompt
1011
0.09
0.39
0,28
CDA
1.63
0.06
0.33
0.58
TBIE
0.08
SDID
2.17
0,11
0.32
0.78
SDID-AVG
4.3
1.71
ITI
0.76
0.11
0.36
0.34
FairQueuee
1.21
0.15
0.61
0.26
Fairlmugen
Pairlmagen-TS
2.17
0.13
0.28
0.67
Fairlmagen-OC
2.27
0.13
Table Standard deviation values across 10 random seeds for gender debiasing experiments. The
results demonstrate the statistical stability and reliability of our experimental findings.
These results validate that Fairlmagen can be readily extended to additional protected attributes beyond the binary categories typically studied in fairness literature, opening opportunities for more comprehensive bias mitigation in ext-to--mage generation.
I Statistical Stability Analysis
To evaluate the statistical reliability of our experimental results, we conduct additional experiments by varying the random seed from 10 and computing the standard deviation for each evaluation metric. As shown in Table 6 the results demonstrate that: (1) the standard deviation values are relatively modest across all methods, indicating consistent and reliable experimental results; (2) Fairlmagen variants exhibit stable performance with standard deviations comparable to other baseline methods, confirming the robustness of our approach; (3) the performance differences between Fairlmagen and baseline methods are substantially larger than the natural variation introduced by random seed changes, validating the statistical significance our improvements; and (4) the impact of our debiasing approach image quality metries remains within acceptable ranges, demonstrating that our method achieves meaningful fairness improvements while introducing quality changes comparable to the inherent stochasticity of the generation process itself.
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Reviews:
[{"review_id": "8NVwLtVWop", "reviewer": "Reviewer_usX8", "summary": "This paper proposes a method for de-biasing stable diffusion across gender and race. They provide distintions between methods in the literature in three main group of Prompt-based, Fine-tuning-based, and Post-hoc editing and chose the latest because of its simplicity and compatibility with a wide range of off-the-shelf diffusion models.", "questions": "- How did you choose the methods to compare in sec 4.4? Why not other methods like [18]?\n-Sec 4.5.1: I am not sure if I follow the claims in this section in the table 5. Which of the methods in table 5 are post-hoc? every method except FairPrompt?\n- Table 5: I see that many numbers are very close to each other., Can you provide other statistics, like std? or run some statistical test to see if the differences are statistically significant? especially for CLIP and MUSIQ\n- 4.5.4: How can we make sure results in Fig 3 are not cherry picked? can you provide ratio of major.minor attributes with all 4 scenarios?\n- Can you provide high-res (1Kx1K) comparison between FairImagen, FairPrompt and the third runner in MUSIQ? You have been using SD3 and I see this as a big advantage but still see small images when comparing quality. We want to know what exactly we lose in the trade-off.\n- What happens to gender neutral professions such as teacher/doctor?", "limitations": "- While table 1 gives a high level overview for tradeoffs between three methods for de-biasing, some of the rows and the way authors put fail (x) and pass (✓) is not clear. For example what is the measure for \"strong bias mitigation\" or \"easy deployment\"? or why we do not see the level of quality degradation for generations as a row? To me it seems that authors designed this table to have most ✓ for post-hoc editing that is the main method behind this paper. Unless otherwise please justify. \n- Evaluation is wide but the depth of each table/section is not high (see questions)\n- Authors mentioned they chose post-hoc method because of \"simplicity and compatibility with a wide range of off-the-shelf diffusion models\", but they did not provide the generalization to other diffusion models.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- minor: table 2-4 are subtable of table 5. either have three tables or one. here we have 4 tables in the text\n- Evaluation is wide but the depth of each table/section is not high (see questions)\n- Comparisons to literature: it is not clear what/why only some methods are selected for comparison. \n\n+ Authors chose a very important problem in the field that has high social impact.  \n+ Authors worked on latest stable diffusion 3 (while I see many papers worked on much earlier versions like SD1.4)", "was_revised": true, "final_justification": "My questions and concerns are well addressed by reviewers, hence increasing my score to 4.", "rebuttal": "**Q1.** How did you choose the methods to compare in sec 4.4? Why not other methods like [18]?\n\n**A1.** We specifically focus on post-hoc methods that operate at inference time without model retraining as indicated in our title \"FairImagen: Post-Processing for Bias Mitigation\". Reference [18] (Kim et al.) is a fine-tuning-based approach that requires retraining parts of the diffusion model, which falls outside of our post-processing scope. We selected comparison methods (TBIE, SDID, ITI-GEN, etc.) that directly compete in the same post-hoc embedding manipulation space to ensure fair evaluation within our targeted paradigm.\n\n**Q2.** Sec 4.5.1: I am not sure if I follow the claims in this section in the table 5. Which of the methods in table 5 are post-hoc? every method except FairPrompt?\n\n**A2.** All methods compared in Tables 2-4 (collectively referred to as Table 5) are post-hoc methods, including Base, FairPrompt, ForcePrompt, SAL, CDA, TBIE, SDID, SDID-AVG, ITI-GEN, and FairImagen. These methods operate at inference time without requiring model retraining. The key distinction is in their implementation approach: some modify prompt embeddings directly (like FairImagen, TBIE, ITI-GEN), whilst others like FairPrompt and ForcePrompt modify the input prompts themselves before embedding generation. All are considered post-hoc as they avoid the computational overhead of fine-tuning the diffusion model.\n\n**Q3.** Table 5: I see that many numbers are very close to each other., Can you provide other statistics, like std? or run some statistical test to see if the differences are statistically significant? especially for CLIP and MUSIQ\n\n**A3.** We thank the reviewer for highlighting the need to assess the stability of our results. We repeated every experiment with 10 independent random seeds (1 – 10) and report the resulting standard deviation for each metric below:\n\n\n\n| Method | Fairness(%) | Accuracy(%) | MUSIQ(%) | Avg(%) |\n|--------|-------------|-------------|----------|--------|\n| Base | 0.61 | 0.05 | 0.39 | 0.24 |\n| FairPrompt | 1.86 | 0.15 | 0.48 | 0.66 |\n| ForcePrompt | 1.04 | 0.09 | 0.41 | 0.29 |\n| SDID-AVG | 4.3 | 0.5 | 0.46 | 1.71 |\n| ITI | 0.76 | 0.11 | 0.36 | 0.34 |\n| FairQueue | 1.21 | 0.15 | 0.61 | 0.26 |\n| FairImagen | 2.37 | 0.13 | 0.28 | 0.74 |\n\nThe results reveal an important finding: the difference between our proposed FairImagen and the Base model falls within 1-2 standard deviation magnitude across the quality metrics. This indicates that the impact of our debiasing approach on image quality is only marginally higher than the natural variation introduced by different random seeds. We consider this trade-off acceptable, as it demonstrates that our method achieves meaningful fairness improvements while introducing quality changes that are comparable to the inherent stochasticity of the generation process itself. This validates the effectiveness of our approach in balancing fairness and quality preservation.\n\n**Q4.** 4.5.4: How can we make sure results in Fig 3 are not cherry picked? can you provide ratio of major.minor attributes with all 4 scenarios?\n\n**A4.** The results in Figure 3 are randomly picked from our generated samples. We randomly selected representative examples from each method's outputs to ensure fair comparison. Regarding the major/minor attribute ratio, since there are more than three demographic groups in our scenarios, simple ratio metrics are not appropriate evaluation measures. Instead, we evaluate fairness quantitatively using the fairness scores presented in Table 5. Figure 3 serves as illustration, while Table 5 provides the comprehensive quantitative evaluation across all methods and scenarios.\n\n**Q5.** Can you provide high-res (1Kx1K) comparison between FairImagen, FairPrompt and the third runner in MUSIQ? You have been using SD3 and I see this as a big advantage but still see small images when comparing quality. We want to know what exactly we lose in the trade-off.\n\n**A5.** Thank you for this good suggestion. We will add high-resolution (1K×1K) comparisons in the next revision. However, due to NeurIPS submission limitations this year, we cannot update the files or provide additional links at this time.\n\n**Q6.** What happens to gender neutral professions such as teacher/doctor?\n\n**A6.** This is an interesting question. Even for seemingly gender-neutral professions like \"doctor,\" we found that Stable Diffusion models tend to predominantly generate white male images. This demonstrates that bias exists even in supposedly neutral occupations, further highlighting the necessity of our work. FairImagen helps mitigate these implicit biases to produce more balanced demographic representations across all professions, including those that should indeed be gender-neutral.\n\n\n**Q7.** How can you justify the measures in Table 1 for \"strong bias mitigation\" or \"easy deployment\"? Why don't we see the level of quality degradation for generations as a row? How can you address the concern that authors designed this table to have most ✓ for post-hoc editing?\n\n**A7.** \"Strong bias mitigation\" refers to the methods ability to effectively reduce demographic biases in generated images. \"Easy deployment\" means that the method can be applied without the need for retraining models. Your suggestion about adding quality degradation level as a row is excellent, and we will include this in the revision. We did not intentionally design this table to favor post-hoc methods. These criteria reflect practical concerns encountered in the literature, in our work and, in aspects we care about. Other researchers might prioritize different criteria based on their specific use cases.\n\n**Q8.** How can you provide generalization to other diffusion encoders since you mentioned choosing post-hoc method because of \"simplicity and compatibility with a wide range of off-the-shelf diffusion models\"?\n\n**A8.** We appreciate the reviewer's insightful question. To demonstrate that FairImagen is indeed an off-the-shelf post-hoc method, we have evaluated our approach with multiple text encoders including T5 and OpenCLIP in addition to the standard CLIP encoder. The results are as follows:\n\n| Method | Fairness | Accuracy | MUSIQ | Avg |\n|--------|----------|----------|-------|-----|\n| Base | 0.0758 | 0.818 | 0.616 | 0.503 |\n| FairImagen | 0.455 | 0.808 | 0.567 | 0.61 |\n| FairImagen-T5 | 0.419 | 0.807 | 0.573 | 0.586 |\n| FairImagen-OC | 0.409 | 0.805 | 0.568 | 0.594 |\n\nThese results demonstrate that our method successfully adapts to different encoder architectures without requiring architecture-specific modifications. The consistent performance across encoders validates our claim of being an off-the-shelf solution—FairImagen can be directly applied to various pre-trained diffusion models regardless of their text encoding choice. This compatibility is achieved because our method operates on the attention mechanisms that are common across these architectures, rather than relying on encoder-specific features. This flexibility makes FairImagen practical for real-world deployment across diverse text-to-image generation systems."}, {"review_id": "r9JyVA1efT", "reviewer": "Reviewer_9e6T", "summary": "The paper tries to target the demographic biases in generation models that originate from CLIP encoder. Comprehensive experiments on fairness benchmarks show FairImagen offers a strong trade-off between fairness and image/prompt quality, outperforming existing post-hoc baselines on Stable Diffusion. The method tries to project the CLIP based prompt embeddings into a “fair” subspace using Fair Principal Component Analysis and aim to minimize the demographic group information while retaining semantic fidelity.", "questions": "1. The description of the method sounds reasonable. However, using it for something like T5 encoders  would help understand the generalizability. What would be the main challenges or steps needed to adapt FairImagen to other embedding architectures?\n\n2.Further, getting other encoders would also help understand how bias in models like SD3 which have mix of both CLIP and T5 is affected by this work. What would be the main challenges of such models with multiple encoders? \n\n3. The empirical noise injection seems helpful, but the choice of noise magnitude and direction remains empirical? Is there a principled way to set or adapt this parameter?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths: \n1. The paper is well written and easy to understand. \n\n2. The motivation of the paper is also quite reasonable given the biases that different components of many models induce. This can help refine many other pipelines as well since it can be used externally as a plugin.\n\n3. Their approach to solve the issue by projec is novel as they claim.\n\n\nWeaknesses: \n1. Their experiments are all restricted to CLIP based image generation model. New models like Stable Diffusion 3 (CLIP + T5) are not used, so it is unclear how generalizable FairImagen is to non-CLIP architectures. \n\n2. Protected attributes focus mainly on binary gender and a set of race/ethnicity categories. Results on broader or more nuanced demographic attributes could help show the generalizability. The work could also try to show some experiments with other complex/entangled concepts.", "was_revised": true, "final_justification": "Accept: The authors addressed the concerns I had in a satsifactory way.", "rebuttal": "**Q1.** What would be the main challenges or steps needed to adapt FairImagen to other embedding architectures like Stable Diffusion 3 (CLIP + T5)? How can you address that experiments are all restricted to CLIP-based image generation models and it is unclear how generalizable FairImagen is to non-CLIP architectures?\n\n**A1.** We appreciate the reviewer's constructive suggestions. To address concerns about generalizability beyond CLIP-based architectures, we have extended our experiments to include alternative text encoders, specifically T5 and OpenCLIP:\n\n| Method | Fairness | Accuracy | MUSIQ | Avg |\n|--------|----------|----------|-------|-----|\n| Base | 0.0758 | 0.818 | 0.616 | 0.503 |\n| FairImagen | 0.455 | 0.808 | 0.567 | 0.61 |\n| FairImagen-T5 | 0.419 | 0.807 | 0.573 | 0.586 |\n| FairImagen-OC | 0.409 | 0.805 | 0.568 | 0.594 |\n\nThe results demonstrate that FairImagen successfully generalizes across different text encoder architectures. All three variants achieve substantial improvements in fairness compared to the baseline (0.0758), with FairImagen-T5 achieving 5.5× improvement and FairImagen-OC achieving 5.4× improvement in fairness scores. Meanwhile, all variants maintain strong image quality metrics, with accuracy scores above 0.805 and MUSIQ scores around 0.57.\n\nThese results validate that our method's core principle—leveraging attention mechanisms to identify and mitigate bias—effectively transfers across different text encoding architectures. This confirms that FairImagen can be readily adapted to emerging text-to-image models regardless of their underlying text encoder, including hybrid architectures like Stable Diffusion 3's CLIP+T5 approach.\n\n\n**Q2.** How can you show results on broader demographic attributes beyond binary gender and race/ethnicity categories?\n\n**A2.** We appreciate the reviewer's valuable suggestion. Adding more complex demographics like age significantly strengthens our paper. We have conducted additional experiments on age debiasing with the following results:\n\n| Method | Fairness | Accuracy | MUSIQ | Avg |\n|--------|----------|----------|-------|-----|\n| Base | 0.136 | 0.816 | 0.627 | 0.527 |\n| FairPrompt | 0.167 | 0.795 | 0.635 | 0.532 |\n| ForcePrompt | 0.106 | 0.791 | 0.625 | 0.507 |\n| SDID-AVG | 0.197 | 0.792 | 0.597 | 0.529 |\n| ITI | 0.152 | 0.808 | 0.575 | 0.511 |\n| FairQueue | 0.182 | 0.791 | 0.632 | 0.535 |\n| FairImagen | 0.227 | 0.784 | 0.58 | 0.53 |\n| FairImagen-T5 | 0.227 | 0.783 | 0.576 | 0.529 |\n| FairImagen-OC | 0.242 | 0.784 | 0.582 | 0.536 |\n\nIt should be noted that due to the inherent difficulty in distinguishing precise ages (e.g., differentiating between 35 and 38 years old), we categorized age into three distinct groups: young, middle-aged, and elderly. The results demonstrate that our proposed FairImagen methods achieve the highest fairness scores (0.227-0.242) among all approaches, representing a substantial improvement over the baseline (0.136). Notably, this improvement in fairness is achieved while maintaining competitive image quality metrics, with MUSIQ scores remaining close to baseline performance. This confirms that our approach effectively generalizes to age debiasing, successfully addressing demographic bias across multiple attributes.\n\n\n\n\n**Q3.** Noise direction and magnitude. \n\n**A3.** The noise direction is not arbitrary. Instead, it is learned from actual group-specific embedding distributions (Section 3.3). We sample from the empirical distribution of how strongly embeddings align with demographic attributes, making it data-driven rather than purely empirical. For the magnitude parameter $\\epsilon$, we provide extensive ablation studies (Figures 2 and Appendix F) showing its effect on the fairness-quality trade-off. We currently tune $\\epsilon$ on a development set. However, we envision that future work could explore adaptive methods based on prompt characteristics or user-specified fairness constraints."}, {"review_id": "8NeqPUt6wc", "reviewer": "Reviewer_VogC", "summary": "This paper discusses that although recent text to image (T2I) model have introduces remarkable improvement in their sample quality and diversity, they also introduce biases. To address this, this work proposes FairImagen — a framework that modifies that the prompt embedding with FairPCA. Additionally, two additional contribution were made i) Noise injection — for improved fairness and pefromance trade-off — and cross demographic debasing — to better handle multiple protect attriubte.", "questions": "1. Based on weakness-1, how does FairImagen perform when compared against the existing SOTA FairQueue?\n- This would be very useful to the research community and work. For example, if FairImagen performance better on fairness but worse on quality, then future research may consider a hybrid of the two approach.\n\n2. Based on weakness-2, How does FairImagen perform when considering more fine-grain protected attributes e.g., Facial expression, or more nuance protected attributes e.g., Skin-Color.\n\n3. What is the main difference between FairImagen and other Post-hoc editing method like ITI-Gen and FairQueue? This was briefly discuss in the related work, but would greatly benefit for a clear one to two liners e.g., FairImage differentiates itself from these approaches by ________ . \n\nOverall, I enjoyed reading this work and thank the author for their time and effort. I would be willing to increase my score, should my concerns be addressed (the idea is good, but the experimental depth needs to be addressed, in my humble opinion). Thank you.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strength \n1. The paper is well-written and structured. Specifically, it the paper flow allows for clear understanding of the problem and solution proposed.\n\n2. The proposed method appears to be very effective (on coarse grained attributes) e.g., table 2 (a) on Gender, which is impressive.\n\n\n## Weakness  \n1. The paper seems not to be up to date with the latest works, e.g., it does not compare against \"NeurIPS24: FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation\", which has been demonstrated to be an improvement of ITI-Gen (which the paper compares extensively against).\n\n- Specifically, FairQueue has demonstrated that ITI-Gen performance degradation is attributed to the quality degradation induced by the technique (which is then addressed by FairQueue). Therefore, having not compared against FairQueue (and instead comparing against only ITI-Gen), it is unclear if FairImagen similarly addresses this issue by ITI-Gen (or is ITI-Gen — the existing SOTA — a better approach).\n\n2. The experiment depth is lacking — both ITI-Gen and FairQueue evaluate against multiple different sensitive attribute demonstrating it's generalizability.\n\n- The limited sensitive attributes experimented on (mostly coarse-grained), e.g., Gender, Race, does not fully demonstrate FairImagen strength and weaknesses. Other SOTA approach like FairQueue and ITI-Gen, in additional to the aforementioned coarse grain attributes also utilize fine-grained attributes e.g., Age (which arguably is more challenging, as it requires preservation of the original sample's features).", "was_revised": true, "final_justification": "The authors have put significant effort in addressing my question. Specifically, they have provided additional experiment with address my major concerns on the incomplete experiments and literature. Overall, I would lean more towards allowing this paper to be published, provided that the authors include these new experiments in their revision.", "rebuttal": "**Q1.** How does FairImagen perform when compared against the existing SOTA FairQueue?\n\n**A1.** Thank you for highlighting FairQueue - an excellent post-hoc approach that addresses key limitations in prompt learning methods. We have now included FairQueue in our comparison, and it demonstrates strong performance over baseline and ITI models as expected, particularly in quality preservation (MUSIQ: 0.621) while achieving meaningful fairness improvements.\n\n| | Fairness | Accuracy | MUSIQ | Avg |\n|---|---|---|---|---|\n| Base | 0.0758 | 0.818 | 0.616 | 0.503 |\n| ITI | 0.152 | 0.803 | 0.58 | 0.512 |\n| FairQueue | 0.197 | 0.809 | 0.621 | 0.542 |\n| FairImagen | 0.455 | 0.808 | 0.567 | 0.61 |\n\n\n**Q2.** How does FairImagen perform when considering more fine-grain protected attributes e.g., Facial expression, or more nuance protected attributes e.g., Age?\n\n**A2.** We appreciate the reviewer's valuable suggestion. Adding more complex demographics like age significantly strengthens our paper. We have conducted additional experiments on age debiasing with the following results:\n\n| Method | Fairness | Accuracy | MUSIQ | Avg |\n|--------|----------|----------|-------|-----|\n| Base | 0.136 | 0.816 | 0.627 | 0.527 |\n| FairPrompt | 0.167 | 0.795 | 0.635 | 0.532 |\n| ForcePrompt | 0.106 | 0.791 | 0.625 | 0.507 |\n| SDID-AVG | 0.197 | 0.792 | 0.597 | 0.529 |\n| ITI | 0.152 | 0.808 | 0.575 | 0.511 |\n| FairQueue | 0.182 | 0.791 | 0.632 | 0.535 |\n| FairImagen | 0.227 | 0.784 | 0.58 | 0.53 |\n| FairImagen-T5 | 0.227 | 0.783 | 0.576 | 0.529 |\n| FairImagen-OC | 0.242 | 0.784 | 0.582 | 0.536 |\n\nIt should be noted that due to the inherent difficulty in distinguishing precise ages (e.g., differentiating between 35 and 38 years old), we categorized age into three distinct groups: young, middle-aged, and elderly. The results demonstrate that our proposed FairImagen methods achieve the highest fairness scores (0.227-0.242) among all approaches, representing a substantial improvement over the baseline (0.136). Notably, this improvement in fairness is achieved while maintaining competitive image quality metrics, with MUSIQ scores remaining close to baseline performance. This confirms that our approach effectively generalizes to age debiasing, successfully addressing demographic bias across multiple attributes.\n\n\n\n\n**Q3.** What is the main difference between FairImagen and other Post-hoc editing method like ITI-Gen and FairQueue?\n\n**A3.** The key differences lie in our methodological approach and technical implementation: (1) **ITI-GEN** learns discriminative token embeddings from reference images by aligning visual and textual embedding differences, then injects these learned tokens after the original prompt. However, this approach can introduce distortions and semantic inconsistencies. (2) **FairQueue** identifies that prompt learning methods like ITI-GEN create abnormalities in early denoising steps and proposes Prompt Queuing and Attention Amplification to modify cross-attention maps during generation. (3) **FairImagen** takes a fundamentally different approach by using FairPCA to project text embeddings into a fairness-aware subspace that explicitly removes group-specific variance before generation begins, offering precise control through the λ parameter and handling multiple demographics simultaneously. We will add detailed technical comparisons and positioning relative to these concurrent works in our camera-ready version."}, {"review_id": "MOeNXaKjRj", "reviewer": "Reviewer_CMAH", "summary": "This paper introduces Fairimagen that reduces bias in text-to-image generation by projecting prompt embeddings onto a fairness-aware subspace that removes explicit group signals. They also introduce controlled noise along group-specific bias directions during inference to preserve semantic meaning while increasing diversity. The experimenst are performed on StableDiffusion3 model on extended Winobias professions dataset with 120 professions.", "questions": "1. SDID originally computes concept vectors in the h-space. However, I believe the authors compared their method to a version that performs this operation in the prompt embedding space. Did you also evaluate how the approach compares to the original SDID?", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths :\n\n1. The approach is simple since the approach requires modified PCA and a simple noise injection during inference.\n2. The approach is intuitive and easy to understand.\n\nWekanesses:\n\n1. I believe the approach struggles to scale effectively to more complex scenarios. For example, the authors present qualitative results for joint gender and race debiasing in Figure 3d, where many CEO images fail to accurately represent the CEO concept and exhibit visual inconsistencies. This suggests that cross-demographic debiasing becomes increasingly challenging as more categories beyond gender and race are introduced.\n\n2. This also ties back to my first point. From the ablation experiments, it seems like the approach is very sensitive to hyperparameters such as noise injection scale, hidden dimension size etc. makes the approach difficult to scale effectively.\n\n2. The approach debiases only the input embeddings at inference but does not tackle biases embedded within the pretrained diffusion model itself. Since the model’s parameters reflect biases from training data, these can still affect outputs, limiting the approach’s overall effectiveness.\n\n3. In Section 4.5.6, the authors demonstrate FairImagen’s robustness to demographically determined prompts, which is impressive. This means that when a prompt clearly and appropriately specifies a gender, FairImagen respects it without unnecessary overrides. However, if the dataset or embedding space strongly associates certain professions or roles with a particular gender (e.g., “doctor” predominantly male), the model may confuse implicit bias for explicit gender information. Doesn’t this imply that even neutral prompts like “doctor” could still generate mostly male images? I don’t see any component in the approach that explicitly prevents this behavior.\n\n4. The technical novelty is limited because the core idea mainly involves integrating FairPCA into the prompt embedding space of Stable Diffusion.", "was_revised": true, "final_justification": "Since most of my concerns were addressed during the discussion phase, I have increased my score. However, I strongly urge the authors to revise the discussion on SDID and offer a more realistic evaluation of the work's limitations, especially regarding the trade-offs between fairness and fidelity.", "rebuttal": "**Q1.** SDID originally computes concept vectors in the h-space. However, I believe the authors compared their method to a version that performs this operation in the prompt embedding space. Did you also evaluate how the approach compares to the original SDID?\n\n**A1.** You are correct in noting this distinction. SDID originally operates in the diffusion model's latent h-space (the semantic latent space within the denoising network), while our implementation adapts it to work in the prompt embedding space for fair comparison with other post-hoc methods including FairImagen. Since FairImagen and other baselines (TBIE, ITI-GEN) operate on CLIP prompt embeddings rather than internal diffusion latent representations, we standardized all comparisons to the prompt embedding space to ensure methodological consistency. We acknowledge that the original h-space SDID implementation might yield different performance characteristics. Evaluating this comparison would be valuable future work to provide a more comprehensive assessment of debiasing approaches across different operational spaces.\n\n**Q2.** How can you address that the approach struggles to scale effectively to more complex scenarios, as shown in Figure 3d where many CEO images fail to accurately represent the CEO concept and exhibit visual inconsistencies?\n\n**A2.** The visual inconsistencies in Figure 3d arise from the inherent trade-off between fairness and semantic fidelity when debiasing across multiple demographic attributes simultaneously. The stronger empirical noise required for joint gender+race debiasing can introduce variation in non-demographic visual elements (background, style). This is a known challenge in fairness-aware generation. Specifically, as we increase demographic diversity, maintaining consistent occupation-specific visual cues becomes difficult. To address this, users can tune the $\\lambda$ parameter and noise scale based on their specific requirements. For applications prioritizing visual consistency, we recommend using lower noise values or debiasing single attributes. We are exploring adaptive noise strategies that preserve occupation-relevant features while diversifying demographics, which would improve scalability to complex scenarios.\n\n**Q3.** How can you address that the approach is very sensitive to hyperparameters such as noise injection scale, hidden dimension size etc. which makes the approach difficult to scale effectively?\n\n**A3.** We acknowledge the hyperparameter sensitivity and provide extensive ablation studies to guide parameter selection. In Section 4.5.2 (Figure 1), we show that hidden dimension size offers a predictable trade-off: lower dimensions (200-500) improve fairness but reduce quality, while higher dimensions (1500-2000) preserve quality but may reintroduce bias. Similarly, Section 4.5.3 (Figure 2) demonstrates that empirical noise scale follows a smooth progression from 0 to 1. To address scalability concerns, we recommend: (1) starting with default values ($d=1000$, $\\epsilon=0.5$) that work well across diverse prompts, (2) using our development set methodology to tune parameters for specific applications, and (3) leveraging the monotonic relationships we observed - parameters don't require exhaustive search but can be adjusted directionally based on desired fairness-quality balance. Future work could explore adaptive parameter selection based on prompt characteristics.\n\n\n**Q4.** How can you address that the approach debiases only the input embeddings at inference but does not tackle biases embedded within the pretrained diffusion model itself?\n\n**A4.** If we only performed fairPCA without adding noise, this would only debias the embeddings, and not resolve biases present within the diffusion model. Unsurprisingly, this doesn't work well, and many biases continue to exist. The addition of noise provides a strong signal that overrides weak biases within the pretrained model in much the same way that prompting \"female doctor\" causes a diffusion model to generate an image of a female doctor, despite the intrinsic bias of the model that causes it to typically generate 'doctor' as male. \n\nThe inability to significantly alter the diffusion model is a fundamental limitation of post-hoc approaches. While the pretrained diffusion model contains learned biases from its training data, our method operates on the interface between text and image generation, which provides significant but not complete control. We chose this approach for practical reasons: (1) it enables immediate deployment without expensive retraining, (2) it works with proprietary models where internal weights are inaccessible, and (3) it allows users to adjust bias mitigation dynamically based on their needs. Our experiments show that despite not modifying the model internals, manipulating prompt embeddings can substantially reduce bias in generated outputs. However, we acknowledge that combining our approach with model fine-tuning could yield even stronger debiasing results. Future work could explore hybrid approaches that leverage both embedding-space and model-space interventions for comprehensive bias mitigation.\n\n**Q5.** How can you prevent neutral prompts like \"doctor\" from still generating mostly male images? What component in the approach explicitly prevents this behavior when the model may confuse implicit bias for explicit gender information?\n\n**A5.** Although \"doctor\" may be neutral to humans, we found that the model tends to generate white male doctors, revealing implicit biases in supposedly neutral occupations. FairImagen addresses this through two key mechanisms: (1) FairPCA projection removes demographic-correlated variance from all embeddings, including neutral prompts, by projecting them into a subspace orthogonal to learned demographic directions. This ensures that embeddings for \"doctor\" lose their implicit association with specific gender/race attributes. (2) The empirical noise injection then adds controlled randomness sampled from the actual distribution of demographic attributes, encouraging diverse outputs even when the original embedding had strong implicit bias. The $\\lambda$ parameter allows fine-tuning this debiasing strength - for heavily biased neutral terms like \"doctor,\" users can increase $\\lambda$ to more aggressively remove the implicit demographic signal while preserving the occupational semantics.\n\n**Q6.** How can you address the concern that the technical novelty is limited because the core idea mainly involves integrating FairPCA into the prompt embedding space of Stable Diffusion?\n\n**A6.** For a good discussion as to why papers such as ours should be seen as novel, please see the article \"Novelty in Science\" by Michael Black.\n\nWhile FairPCA is an established technique, our contribution lies in adapting and extending it specifically for the text-to-image generation domain. Our technical contributions include: (1) Applying FairPCA to prompt embeddings in diffusion models, which hasn't been explored before. (2) Introducing empirical noise injection to prevent over-neutralization of outputs. (3) Demonstrating how to handle multiple demographic attributes simultaneously. (4) Providing extensive empirical evaluation showing the effectiveness of this approach across different bias scenarios. The value of our work is in showing that simple, principled methods can achieve strong bias mitigation without model retraining, making fair image generation accessible to practitioners who cannot afford to retrain large diffusion models."}]

Author rebuttal:

Meta-review: This paper presents FairImagen, a method to reduce gender and race bias in text-to-image models. The approach modifies text embeddings at inference time using FairPCA and controlled noise injection. This avoids the need for costly model retraining. The idea is simple, tackles a very important social issue, and works with modern models like SD3. The main weakness was the initial evaluation, which reviewers found to be limited in scope. However, the core contribution is solid and addresses a critical need. 

During the discussion period, reviewers raised several important concerns. The major ones were about the method's generalizability beyond CLIP-based models (usX8, 9e6T) and its performance on more complex attributes like age (9e6T, VogC). Reviewers also requested comparisons to more recent work like FairQueue (VogC) and asked for statistical validation of the results (usX8). The authors provided an informative rebuttal with all requested info. They showed that FairImagen works with T5 and OpenCLIP encoders and can reduce age-related bias. They also added the requested comparison and statistical analysis. These thorough responses effectively resolved the reviewers' primary concerns, leading them to raise their scores. I agree with the reviewers and recommend accepting the paper, as the authors convincingly addressed the reviewers’ questions.
Decision: Accept (poster)