AI-generated faces influence gender stereotypes and racial homogenization

## Abstract

Abstract Text-to-image generative AI models such as Stable Diffusion are used daily by millions worldwide. However, the extent to which these models exhibit racial and gender stereotypes is not yet fully understood. Here, we document significant biases in Stable Diffusion across six races, two genders, 32 professions, and eight attributes. Additionally, we examine the degree to which Stable Diffusion depicts individuals of the same race as being similar to one another. This analysis reveals significant racial homogenization, e.g., depicting nearly all middle eastern men as dark-skinned, bearded, and wearing a traditional headdress. We then propose novel debiasing solutions that address the above stereotypes. Finally, using a preregistered experiment, we show that being presented with inclusive AI-generated faces reduces people’s racial and gender biases, while being presented with non-inclusive ones increases such biases. This persists regardless of whether the images are labeled as AI-generated. Taken together, our findings emphasize the need to address biases and stereotypes in AI-generated content.

## Introduction

Artificial intelligence (AI) biases refer to the systematic and unfair preferences or prejudices embedded in AI systems, often reflecting the biases present in the data used to train these systems []. Such biases can perpetuate and even exacerbate societal inequalities, as the AI algorithms may inadvertently discriminate against certain groups. One glaring example of AI bias is the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm, used by judges and parole officers in the U.S. to assess the likelihood of recidivism. This algorithm, which has been used on over a million offenders to date, has been shown to produce racially biased predictions [, ].
Another notable example of AI bias is found in face recognition algorithms, which have been shown to discriminate based on race and gender [].
Amazon’s endeavor to use an algorithm to evaluate job candidates based on their resumes is yet another example of AI bias;
this algorithm reinforced gender preconceptions by penalizing resumes that included phrases associated with women [].
In the context of natural language processing, notable biases have been reported in popular word embedding models such as BERT and GPT-2. Such models often associate certain occupations or stereotypes more strongly with one gender or racial group than another [].

In this study, we focus on racial and gender stereotypes in Stable Diffusion [], one of the most popular text-to-image generative models, used daily by millions worldwide []. Recent studies have demonstrated that Stable Diffusion underrepresents certain races or genders [, , ], but none of these studies proposed debiasing solutions. Moreover, none of them offered a comprehensive examination of such biases across racial groups, genders, professions, and attributes. Other studies have proposed debiasing solutions [, ], but these are either not automated, or are unable to generate images that adequately represent complex prompts; see the Related Work section for more details. Another form of stereotype that has been completely overlooked in the literature is when individuals of the same race are depicted as being too similar to one another, e.g., when Middle Eastern men are all depicted as being bearded and dark-skinned, or when Middle Eastern women are all depicted as wearing a traditional headcover, or “hijab”. Consequently, it remains unclear whether such stereotypes (if they exists) can be addressed by diversifying the facial features of same-race individuals. Other open questions that have not been addressed to date are whether being exposed to AI-generated faces can affect people’s racial and gender biases, and whether the AI label (i.e., the fact that the images are labeled as AI-generated rather than human-generated) plays a role in this phenomenon.

To address these questions, we start off by developing a state-of-the-art classifier to predict race and gender, allowing us to quantify biases in Stable Diffusion-generated images across six races, two genders, 32 professions, and eight attributes. We then propose a novel debiasing solution, and demonstrate its ability to outperform state-of-the-art alternatives across various benchmarks. Additionally, using a measure of image similarity, we reveal that Stable Diffusion exhibits a high degree of stereotyping, depicting individuals from certain racial backgrounds as being too similar to one another. We address this issue by proposing another novel solution, which successfully increases facial diversity across genders and racial groups. Finally, using preregistered randomized controlled trials, we show that being exposed to inclusive AI-generated faces (such as those generated by our model) can reduces people’s racial and gender biases, while exposure to non-inclusive AI-generated faces (such as those generated by Stable Diffusion) can increase these biases. Our experiment provides no evidence that the AI label plays a significant role in this phenomenon, indicating that the observed effect is entirely driven by the image’s content, irrespective of its source.

## Results

### Classifying gender and race

To examine the biases in Stable Diffusion, we start off by developing a novel classifier that predicts the race and gender of any given face image. Our classifier is composed of three stages: face detection, face embedding generation, and the classification stage; see Material and Methods for technical details. To train and validate our classifier, we utilize FairFace []—one of the largest publicly-available datasets of face images. FairFace specifies the race (Black, East Asian, Indian, Latinx, Middle Eastern, Southeast Asian, and White), and the gender (Female, Male)
of each image. We simplified FairFace’s categorization by combining the East Asian and Southeast Asian categories into a single one (Asian);
see the Materials and Methods section for more details.

We trained our classifier on FairFace’s training set, and evaluated it using FairFace’s validation set. Additionally, we benchmarked our classifier against several alternatives from the literature, namely: CLIP’s zero-shot [], Google’s FaceNet [] + SVM [], FairFace’s ResNet-34 [], EfficientNet-B7 (tuning all layers) [], and Large Vision Transformer VIT (tuning all layers) [].
Supplementary Note 1 summarizes the results for race and gender, respectively. These results show that, across both categories, our classifier consistently achieves state-of-the-art performance in terms of accuracy, precision, recall, and F1 score.

### Examining underrepresentation in Stable Diffusion

Armed with our state-of-the-art classifier, we are now ready to examine the degree to which different races and genders are represented in SDXL-generated images. To this end, we generated 10,000 images using the following racial- and gender-neutral prompt: “a photo of a person”. The distribution of the resulting images is summarized by the dashed bars in Figure 1. As can be seen, White is the most represented race (47% of images), followed by Black (33%) The remaining races are all extremely underrepresented, e.g., only 3% are Asian, and only 5% are Indian. As for gender, males are overrepresented, constituting 65% of all images. These findings mirror what has been reported by Ghosh and Caliskan [], showing that most images generated by Stable Diffusion (v2.1) representing the prompt “a front-facing photo of a person” depict light-skinned Western men.

One possible explanation behind these biases could be that SDXL is merely reflecting the biases already present in the dataset on which it was trained, namely LAION-5B []. To examine this possibility, we used a subset of LAION-5B [] consisting of 88,714 images; see Material and Methods for more details. The distribution of those images is summarized by the plain bars in Figure 1. As can be seen, White is even more represented in LAION-5B than SDXL (63% vs. 47%), while all remaining races are underrepresented. As for gender, both male and female individuals are equally represented with a 50-50 split. This indicates that SDXL contains its own biases beyond just the data it was trained on, e.g., raising the percentage of Black individuals from 11% to 33%, and raising the percentage of males from 50% to 65%.

One way to address the aforementioned biases is to develop a model that generates images in which all races are equally represented. To this end, we introduce a fine-tuned version of SDXL, which we call: “SDXL-Inc” (where Inc is a shorthand for Inclusion). This model was created as follows. First, we identified 32 professions, and split them into 21 that are used for fine-tuning, and 11 that are used for testing (these professions are listed in Supplementary Table 1). Then, for every combination $(X,Y)$ such that $X\in$ {Black, White, Asian, Indian, Latinx or Hispanic, Middle Eastern} and $Y\in$ {male, female}, we generated a separate dataset consisting of images depicting the 21 professions.
The generation of these 12 datasets (6 races × 2 genders) was done using the following prompt: “a photo of $X$ $Y$ $Z$ looking at the camera, closeup headshot facing forward, ultra quality, sharp focus”, where $Z$ denotes one of the 21 professions. After that, we fine-tuned SDXL with each dataset, yielding 12 different sets of weights. The fine-tuning was done using LORA (Low Rank Adaptation) [], which reduces the number of trainable parameters for the downstream task; see Materials and Methods for more details. The final model (SDXL-Inc) randomly selects one of the 12 sets of weights in response to any given prompt.

To evaluate SDXL-Inc, we used it to generate 10,000 images, relying on the same prompt used earlier, i.e., “a photo of a person”. The distribution of the resulting images is summarized by the starred bars in Figure 1. As shown in this figure, all races are almost equally represented, and the differences between them are markedly smaller than the differences present in both LAION-5B and SDXL. As for gender, SDXL-Inc is able to represent males and females equally, unlike SDXL.

Figure: Figure 1: Examining biases in LAION-5B, Stable Diffusion XL (SDXL), and our SDXL-Inc. Comparing the representation of different races and genders in a random sample of 88,714 images from the LAION-5B dataset, and in a sample of 10,000 images generated by Stable Diffusion XL using the prompt: “a photo of a person”. The same prompt is then used to generate 10,000 images by our SDXL-Inc.

### Examining professional stereotypes in Stable Diffusion

Let us now shift our attention to professional stereotypes in SDXL. To this end, we used SDXL to generate 320,000 images depicting 32 different professions (10,000 per profession). More specifically, for each profession, $Z$, we used the following prompt: “a photo of a $Z$, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus”. Moreover, to specify the types of images that we want to avoid, we used the following negative prompt: “cartoon, anime, 3d, painting, b&w, low quality”. For each profession, we used our classifier to examine the racial- and gender- composition of the resulting images.

Figure: Figure 2: Professional stereotypes in Stable Diffusion XL (SDXL). Given 25 professions, SDXL was used to generate 10,000 images per profession. a, Racial distribution per profession. b, Gender distribution per profession.

Figure: Figure 3: Results of SDXL-Inc. Given eight attributes and eight professions, both SDXL and SDXL-Inc were used to generate 10,000 images per profession and per attribute. a, Race distribution per attribute, with the upper row corresponding to SDXL, and the lower row corresponding to SDXL-Inc. b, The same as (a) but for professions instead of attributes. c, Gender distribution per profession and per attribute for SDXL and SDXL-Inc. The standard deviation(s) corresponding to each subplot is denoted by $\sigma$ followed by a subscript indicating the model.

We start off by examining racial stereotypes in each profession. Figure 2a depicts the results for the 25 professions, while Supplementary Figure 2 depicts the remaining seven professions. Numeric values below 15% are omitted to improve the visualization; see Supplementary Table 2 for all values. As can be seen, in 18 out of the 25 professions, the percentage of White faces surpasses that of all other races combined, even exceeding 80% in some cases. White also happens to be the most commonly represented race in 21 out of the 25 professions.
As for the remaining four, two of them are typically low-income jobs, namely Cleaner and Security Guard, and both are mostly represented by images depicting Black individuals. Similarly, when considering the other seven professions (Supplementary Figure 2), we find that TV presenter is mostly White, while Janitor and Garbage Collector are mostly Black and Middle Eastern, respectively. These findings confirm the findings of Bianchi et al. [], showing that prestigious, high-paying professions are often represented as White.

Having examined the racial distribution, we now shift our attention to the gender distribution. The results are depicted in Figure 2b; and the exact percentages of males and females are listed in Supplementary Table 2. As can be seen, males represent 90% of the images in 16 different professions, including Doctor, Manager, and Professor. Moreover, the percentage of males exceeds that of females in 20 out of the 25 professions. As for the remaining five, they include Nurse and Secretary—two common stereotypes of women. Similar findings were reported in the literature, showing that AI-generated images tend to associate certain jobs with men and certain other jobs with women [, ].

### Debiasing Stable Diffusion across professions and attributes

Having examined stereotypes in terms of profession, we now turn to stereotypes in terms of the following attributes: Winner, Beautiful, Intelligent, Parent, Sibling, Terrorist, Poor, and Criminal. The results of this analysis are depicted in the upper row of Figure 3a, and the corresponding numerical values are listed in Supplementary Table 5. We find that White is extremely overrepresented in the three attributes that tend to be associated with success and attractiveness, i.e., Winner, Beautiful, and Intelligent. White is also extremely overrepresented in the two family-related attributes, i.e., Parent and Sibling. In contrast, when it comes to Terrorism, Middle Eastern are the most represented race, and none of the images depict a White individual, reinforcing existing stereotypes connecting Middle Easterners to terrorism []. Similarly, when it comes to crime and poverty, the majority of images depict Black individuals, reinforcing common stereotypes in this regard []. Similar findings were reported by Bianchi et al. [] when analyzing Stable Diffusion (v1-4), showing that attractiveness is associated with White people, thugs are associated with Black people, and terrorists are associated with Brown men with beards.

Having established that SDXL exhibits biases in terms of attributes, we now evaluate SDXL-Inc’s ability to overcome these biases. To this end, we repeated the same procedure used earlier with SDXL, but using our SDXL-Inc model instead. The results for professions are depicted in the bottom row of Figure 3a. As can be seen, the underrepresentation of different races is markedly reduced compared to SDXL, as evidenced by the substantial reduction in standard deviation ($\sigma$). Importantly, none of the eight attributes considered in this analysis were used during the fine-tuning phase, and yet SDXL-Inc was able to significantly reduce the racial biases related to them. This indicates that our solution can indeed be generalized beyond the features it was fine-tuned for.

Let us now reexamine professional stereotypes to further evaluate SDXL-Inc’s debiasing capability. To this end, we selected four of the professions that were used during the fine-tuning phase (Dietitian, Manager, Pharmacist, and Pilot), as well as four professions that were not used during that phase (Accountant, Journalist, Musician, and Firefighter) to further assess the generalizability of SDXL-Inc. For each profession, Figure 3b shows the racial distribution of images using SDXL (upper row) and SDXL-Inc (lower row). As can be seen, regardless of whether the profession is Black-dominated (Musician and Firefighter) or White-dominated, our solution is able to significantly reduce racial biases. Again, the reduction in standard deviation is substantial, reaching multiple folds across all professions. Similar trends are observed when examining the remaining 24 professions considered in our analysis; see Supplementary Figure 3a.

Finally, we evaluate SDXL-Inc’s ability to address gender biases across the aforementioned attributes and professions. As can be seen in Figure 3c, SDXL (solid bars) exhibits substantial biases, e.g., depicting the vast majority of beautiful individuals as female while depicting the vast majority of intelligent individuals as male. In contrast, our solution consistently produces an equal, or near-equal, split between female and male (dashed bars), regardless of the attribute or profession. These performance improvements are reflected by the vast reduction in standard deviation, from 40.3 to just 2.7. In fact, for the remaining 24 professions, the standard deviation corresponding to our solution drops even further, reaching 1.0; see Supplementary Figure 3b.

In addition to SDXL-Inc, we experimented with an alternative debiasing solution that follows a fundamentally different approach, which is to use a Large Language Model, namely GPT-4 [], “in-the-loop”; see Materials and Methods for technical details. The results of this solution are depicted in Supplementary Figure 4, and the corresponding numerical values are listed in Supplementary Table 6. As can be seen, this solution is also capable of drastically reducing the race and gender biases exhibited by SDXL.

Figure: Figure 4: Results of SDXL-Div. a, For each ethnicity, the distribution of cosine similarity of images generated using SDXL (plain) and SDXL-Div (dashed). b and c, Sample images of Middle Eastern individuals generated using SDXL and SDXL-Div, respectively.
Refer to caption: extracted/2402.01002v2/figures/kde/kde_White.png

### Addressing racial homogenization

We aimed to develop a version of SDXL capable of generating images with greater facial diversity per race compared to the original version of SDXL. To this end, we downloaded the images hosted on the Flickr-Faces-HQ dataset, which hosts high-resolution (1024×1024) images of human faces with “considerable variation in terms of age, race, and image background” []. Since the dataset is unlabeled, we used our classifier to infer the race of each image; see Supplementary Table 4 for the number of images per race. The resulting labelled dataset was then used to fine-tune SDXL. Again, the fine-tuning was done using LORA [] to reduce the number of trainable parameters for the downstream task. We call the resulting model “SDXL-Div” (where Div is a shorthand for Diversity); see Materials and Methods for technical details.

To evaluate SDXL-Div’s ability to increase the facial diversity of SDXL, each of the two models (SDXL and SDXL-Div) was used to generate $\sim$10,000 images per race. Then, our racial classifier was used to obtain an embedding for each image. Finally, we computed the cosine similarity between every pair of images that have the same race and are generated by the same model, resulting in $\sim$50 million cosine similarity values per race per model. The results of this analysis are depicted in Figure 4a. As can be seen, SDXL-Div is indeed able to increase the facial diversity of SDXL, regardless of the race. The greatest difference is observed in the case of Middle Eastern individuals (dropping the mean cosine similarity from 0.61 to 0.41) and Latinx individuals (from 0.55 to 0.39). To help the reader appreciate the difference in diversity between the two models, Figures 4b and 4c provide sample images of Middle Eastern individuals generated using SDXL and SDXL-Div, respectively. As can be seen, images generated using the latter model have markedly greater facial diversity compared to those generated using the former model.

### User study

We ran four user studies to determine whether exposure to AI-generated faces can affect people’s racial and gender biases. We estimated that to obtain a power of 0.8 to detect a medium effect size (Cohen’s d) of 0.5 in a paired-sample comparison, a sample of 135 participants would be needed. We recruited participants on Prolific, with prescreening settings of US resident and English as native language. Additionally, we prohibited participation more than once in our experiment. These studies were preregistered at AsPredicted ([https://aspredicted.org/3v3wt.pdf](https://aspredicted.org/3v3wt.pdf)). All studies were approved by the Institutional Review Board at New York University Abu Dhabi under the category of Exempt or Expedited Research (HRPP-2024-53).

In each of the four studies, participants are presented with six AI-generated images, answer a few questions about each image, and then answer an overall question. Each study has four different conditions, depending on whether the images are inclusive or not, and whether participants are informed that the images are generated by AI or are produced by an artist. Next, we provide an overview of the four studies; see Supplementary Note 3 for the exact wording and images used therein.

Study 1 examines racial bias. In particular, given a profession $P\in\{\textnormal{chef},\textnormal{dietitian},\textnormal{journalist}\}$, participants are presented with six images depicting $P$. For each image, they are asked to determine the age, perceived gender, and perceived race of the person depicted in the image. Finally, after seeing all six images, participants answer the question $Q_{1}$: What percentage of $P$ in the US are White?
Here, the non-inclusive images are generated using SDXL and they all depict white individuals, whereas the inclusive images are generated using SDXL-Inc and each of the six races are depicted in a separate image. In both conditions, three images depict men, while the other three depict women. All images can be seen in Supplementary Figure 7.

Study 2 examines gender bias, and is similar to Study 1 apart from two modifications: (i) The professions are $\{\textnormal{accountant},\textnormal{math scientist},and\textnormal{tailor}\}$; (ii) after seeing all six images, participants answer the question $Q_{2}$: What percentage of $P$ in the US are men?
Here, the non-inclusive images are SDXL-generated, and all depict men, while the inclusive images are generated by SDXL-Inc and half depict men while the other half depicts women. In both conditions, each of the six races is depicted in a separate image; see Supplementary Figure 9.

Study 3 examines racial homogenization. Specifically, participants are presented with six images depicting Middle Eastern men. For each image, they are asked to describe the age, skin tone, and facial hair of the man featured in the image. After seeing all six images, participants answer the question $Q_{3}$: What is your estimation of the percentage of Middle Eastern men who have beards?
Here, the non-inclusive images are SDXL-generated, and all depict bearded Middle Eastern men. In contrast, the inclusive ones are generated by SDXL-Div, and depict men with varying levels of facial hair; see Supplementary Figure 11

Study 4 also examines racial homogenization, but focuses on women instead of men. In particular, participants are presented with six images depicting Middle Eastern women, and are asked to describe the age, skin tone, and head cover of the woman featured in the image. Finally, participants answer the question $Q_{4}$: What is your estimation of the percentage of Middle Eastern women who wear headcovers? Here, the non-inclusive images are generated by SDXL, and depict Middle Eastern women that are all wearing a head cover. On the other hand, the inclusive ones are generated by SDXL-Div and depict Middle Eastern women, half of whom are wearing a head cover while the other half are showing their hair.

Figure: Figure 5: User study results. Subfigures a to d summarize the participants’ responses in Studies 1 to 4, respectively. Boxes extend from the lower to upper quartile values, with a horizontal line at the median; whiskers extend to the most extreme values no further than 1.5 times the interquartile range from the box. P values are calculated using the t-test, unless one of the groups does not pass the Shapiro–Wilk test, in which case P values are calculated using the Mann-Whitney U test. ^∗p$<$0.05; ^∗∗p$<$0.01; ^∗∗∗p$<$0.001; ^∗∗∗∗p$<$0.0001; $ns=$ not significant).

Additionally, for each question $Q_{i}:i\in\{1,2,3,4\}$, we recruited 135 participants from Prolific to answer $Q_{i}$ without being presented with the six AI-generated images. Figures 5a to 5d summarize the responses to questions $Q1$ to $Q4$, respectively. For all four questions, exposure to images generated by SDXL-Inc reduces bias compared to the baseline in which participants are not exposed to AI-generated images. In contrast, exposure to SDXL-generated images increases bias (compared to the baseline) for all questions apart from $Q_{1}$ where the effect is not significant. Next, we examine the effect of the AI label. As can be seen in the figure, there is no significant difference in participants’ responses when the images are labelled as being produced by an artist rather than being AI-generated. This holds for both SDXL and SDXL-Inclusive. Thus, we find no evidence that the AI label plays a role in the aforementioned phenomenon.

## Discussion

We set out to examine the stereotypes and biases in Stable Diffusion XL (SDXL), a text-to-image generator used daily by millions worldwide []. To the end, we developed a classifier to predict the race and gender of any given face image, and demonstrated that it achieves state-of-the-art performance. Using this classifier, we showed that the vast majority of faces generated by SDXL are White males (as previously shown by Ghosh and Caliskan []), while certain racial groups are extremely underrepresented, e.g., only 3% are Asian and only 5% are Indian. Biases were also found when considering various attributes, e.g., associating beauty with femininity and intelligence with masculinity. Some of these biases are less severe in the dataset on which Stable Diffusion was trained, suggesting that certain biases are further exacerbated by the model itself.

Biased text-to-image models may contribute to the normalization of gender stereotypes, potentially shaping societal attitudes towards the roles and capabilities of women in various professions. For instance, as we have demonstrated, Stable Diffusion mostly depicts secretaries and nurses as women while depicting managers, doctors, and professors as men; similar gender stereotypes were reported in the literature [, ]. Bearing in mind that millions of people worldwide are already using such models daily, addressing gender stereotypes in these models can be crucial. As suggested by Study 2, such stereotypes can be reduced by an inclusive model and can be exacerbated by a non-inclusive one, indicated the potential of AI in alleviating gender inequality.

Biased representations of gender and race may contribute to the creation of content that not only misrepresents certain groups but may also perpetuate discriminatory practices. This could be detrimental in the context of advertising, marketing, and media campaigns, where visuals hold substantial influence. For example, as our analysis has shown, Stable Diffusion associates low-income jobs such as Cleaner, Janitor, and Security Guard with Black people, while associating higher-prestige jobs such as Doctor, Lawyer, and Professor with White people. These findings reflect what has been reported by Bianchi et al. [], showing that several of the most prestigious, high-paying professions are represented by Stable Diffusion (v1-4) as White. Another example of bias is the association between crime and Black people, as well as the assassination between terrorism and Middle Easterners, which may reinforce existing biases against these racial groups [, ]. As we have seen in Study 1, whether or not the model is inclusive can affect people’s perception of the racial distribution of certain professions, and this effect is likely to grow more pronounced as the use of AI-generated images becomes more widespread.

Stable Diffusion’s portrayal of people from any given race as resembling one another may reinforce existing racial stereotypes. For instance, as we have demonstrated, Stable Diffusion mostly depicts Middle Eastern men as dark-skinned, bearded, and wearing a traditional headdress, and mostly depicts Middle Eastern women as dark-skinned, wearing a headscarf. Such oversimplified and generalized depictions of a particular racial group can be culturally insensitive, and may misrepresent the true diversity within that group. They may also lead to feelings of alienation, low self-esteem, and a sense of being misunderstood. As we have demonstrated in Studies 3 and 4, racial homogenization can be reduced using inclusive models, and can be exacerbated using non-inclusive ones.

### Related Work

Compared to all the works discussed in this section, ours is the only one that examines, and addresses, the problem of racial homogenization in AI-generated faces, i.e., the depiction of individuals from a specific racial or ethnic group as too similar in appearance. We are also the first to conduct a randomized control trial to understand the impact of being exposed to inclusive and non-inclusive AI-generated faces, and whether the AI-label plays a role in this phenomenon. Next, we summarize the relevant papers, and discuss any additional differences that may exist between our work and theirs.

Bianchi et al. [] used a previous version of Stable Diffusion (v1-4) to examine biases across ten professions and three races, but did not propose a debiasing solution. They classified the race and gender of any generated image as follows. First, for each of the five demographic categories considered (i.e., Male, Female, White, Black, Asian), they took the images that represent this category in the Chicago Face Dataset [], and fed those images to CLIP (the core representational component of Stable Diffusion) to generate vector representations. These were then averaged to obtain a single archetypal vector representation of the category (e.g., a single vector for Black). Any image can then be classified as $X$ if its vector representation is most closely aligned (in cosine distance) to the archetypal vector representation of $X$ (e.g., Black) compared to the alternative categories (e.g., Asian or White). The authors used this classifier to analyze 100 images per occupation, and found professional stereotypes that reinforce racial and gender disparities. Compared to our work, the authors did not consider certain racial groups, such as Indian, Latinx, and Middle Eastern. They also did not propose debiasing solutions.

Ghosh and Caliskan [] used a previous version of Stable Diffusion (v2.1) to analyze biases across three genders and 27 nationalities, but did not consider professions nor proposed debiasing solutions. Using CLIP’s cosine similarity, they compared images generated using the prompt: ‘a front-facing photo of a person’ against those generated using the prompt: ‘a front-facing photo of $X$’ where $X$ is either a gender (i.e., a man, a woman, a nonbinary gender) or a nationality (e.g., a person from Brazil). The authors then manually classified the images based on skin tone (light vs. dark) and found that most generated images were light-skinned. Compared to our work, the authors did not consider professions (e.g., Doctor, Nurse, etc.) nor attributes (e.g., Beautiful, Intelligent, etc.) in their analysis. Moreover, in the context of race, the authors manually classified skin-color rather than proposing a classifier, and only provided qualitative rather than quantitative conclusions. Finally, the authors did not propose debiasing solutions.

Wang et al. [] studied the association of pleasant and unpleasant attributes with certain concepts such as: European American and African American names, Light Skin and Dark Skin, Straight and Gay. In addition, they studied gender stereotypes across eight professions. Compared to our work, the authors did not propose any debiasing solutions. They also did not consider racial groups apart from European American (White) and African American (Black).

Friedrich et al. [] proposed a “user in control” solution, called Fair Diffusion, which utilizes a textual interface allowing users to instruct generative image models on fairness. Intuitively, this is made possible by extending classifier-free guidance [] with an additional fair guidance term, which depends on additional fairness instructions provided by the user. This approach requires no data filtering nor additional training. The authors used their approach to analyze images generated by a previous version of Stable Diffusion (1.5), focusing on professional stereotypes across genders. Compared to our work, the authors did not consider racial stereotypes, and did not consider a fully automated solution.

Zhang et al. [] proposed a solution called ITI-GEN (inclusive Text-to-Image Generation), designed to generate images that are uniformly distributed across attributes of interest. One such attribute could be, e.g., “gender”, in which case the images generated by ITI-GEN would be equally split between the various attribute categories, e.g., “male” and “female”. More specifically, given an input prompt and some desired attribute(s), the model learns discriminative token embeddings representing each category of each attribute. It then injects these learned tokens after the original prompt, thereby synthesizing a set of prompts, each representing a unique combination of categories belonging to different attributes. Finally, this set is used to generate an equal number of images for any category combination. The authors used their model to analyze a previous version of Stable Diffusion (v1-4), focusing on 40 physical attributes (each consisting of two categories, one positive and one negative), along with gender, skin tone, and age.

Compared to our work, Zhang et al. focused on skin tone rather than race. Thus, unlike our analysis, theirs does not distinguish between, say, Asian and White individuals who happen to be equally light-skinned, or between Indian and Latinx individuals who happen to have similar skin tones. The authors also focus on physical attributes (e.g., Black hair, Mustache, etc.) as opposed to those describing various characteristics like the ones used in our analysis (e.g., Criminal, Intelligent, Parent, Poor, etc.). Additionally, their analysis of professional stereotypes focuses on four professions (Professor, Doctor, Worker, Firefighter) and 200 images per profession, while we focus on 32 professions and 10,000 images per profession. Finally, as discussed in Supplementary Note 2, their solution is unable to debias complex prompts, such as “a person with green hair and eyeglasses”, or “a person with the Eiffel tower”. This difference in performance could (at least partially) be explained by the fact that their solution has a higher error rate when it comes to generating images of a certain race (see Supplementary Table 9).

## Materials and Methods

### Data Overview

This section describes the datasets used in our study.

I) The LAION-5B dataset: This is the dataset that was originally used to train Stable Diffusion []. We utilized a subset of this dataset, consisting of high-resolution images [], to determine whether the biases in Stable Diffusion XL can be entirely attributed to the data on which it was trained. We randomly selected 172,923 images from this subset, and kept those having one or more of the following keywords: face, person, child, woman, or man. We then cropped the images to retain only the face(s) appearing therein, and discarded any resulting face images that are smaller than $100\times 100$ pixels. This filtering process left us with a final set of 88,714 face images.

II) The FairFace dataset: This is one of the largest publicly-available datasets of face images. For each image, the dataset specifies the race (Black, East Asian, Indian, Latinx, Middle Eastern, Southeast Asian, and White), and the gender (female, male) [].
Moreover, the dataset is readily divided into two sets: 86,744 images for training and 10,954 for validation. We combined East Asian and Southeast Asian into a single category: Asian.
The resulting dataset was then used to train and validate our race and gender classifiers. For race, the number of images used for validation was: 1556 for Black; 2965 for Asian; 1516 for Indian; 1623 for Latinx or Hispanic; 1209 for Middle Eastern; and 2085 for White. As for gender, the number of images used for validation was: 5162 for female; and 5792 for male.

III) The Flickr-Faces-HQ dataset: This dataset consists of 70,000 high-resolution ($1024\times 1024$) images of human faces crawled from Flickr with considerable variation in terms of age, race, and image background []. It is unlabeled, and was originally created as a benchmark for generative adversarial networks (GAN). We utilized this dataset to fine-tune our SDXL-Div model in order to generate face images with varying races and facial features. This was done to overcome the fact that, for certain races, the images generated by Stable Diffusion XL seem too similar to one another.

IV) The Stable Diffusion validation dataset: This dataset consists of images that we have generated using Stable Diffusion XL. In particular, the dataset consists of 10,000 images per race and 10,000 images per gender.
For race, the prompt used to generate the images was: “a photo of a $X$”, where $X\in$ {Asian, Black, Indian, Latino or Hispanic, Middle Eastern, White}. As for gender, the prompt used was: “a photo of a $X$”, where $X\in$ {female, male}.
This dataset was used to validate our race and gender classifiers.

V) The SDXL-Inc fine-tuning dataset: This dataset consists of Stable Diffusion-generated images with varying race, gender, and profession. The images were generated using the prompt: “a photo of a $X$ $Y$ $Z$, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus”, where $X\in$ {Asian, Black, Indian, Latino or Hispanic, Middle Eastern, White}; $Y\in$ {female, male}; and $Z$ is one of the 21 professions listed in Supplementary Table 1 under the “fine-tuning” category. For any of the twelve combinations of race and gender ($X$, $Y$), we compiled the corresponding images from all 21 professions into a single dataset, which was used to fine-tune a version of SDXL tailored specifically for race $X$ and gender $Y$. This process yielded 12 fine-tuned models (one per race-gender combination); these are the 12 components constituting our SDXL-Inc solution.

VI) The profession dataset: This dataset consists of Stable Diffusion-generated images depicting 32 professions. More specifically, we generated 10,000 images per profession using the prompt: “a photo of $Z$, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus”, where $Z$ is one of the 32 professions listed in Supplementary Table 1. In this table, the 21 professions listed under the “fine-tuning” category were used to fine-tune SDXL, while the remaining 11 professions (i.e., those listed under “generalization testing”) were used to evaluate the generalization capability of SDXL-Inc.

VII) The attribute dataset: This dataset consists of Stable Diffusion-generated images depicting eight attributes. In particular, we generated 10,000 images per attribute using the prompt: “a photo of a $X$, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus”, where $X\in$ {Poor, Winner, Beautiful, Intelligent, Parent, Sibling, Terrorist, Criminal}. This dataset was used to evaluate the generalization capability of SDXL-Inc.

### Methods

This section presents the tools and techniques used in this work, including Stable Diffusion, face detection, face embedding generation, classification models, as well as the use of GPT-in-the-loop.

#### Stable Diffusion

Stable Diffusion is a text-to-image generative model used to generate images based on prompts fed as input. A recent state-of-the-art version, called Stable Diffusion XL (SDXL), has a second text encoder and 3x larger UNet backbone, which increases the number of parameters []. Additionally, SDXL has multiple novel conditioning schemes, as well as a refinement model that improves the visual fidelity of generated images. In our work, we utilized SDXL due to its significantly improved ability to generate human faces compared to its predecessors. We used the Hugging Face repository “stabilityai” and the model “stable-diffusion-xl-base-1.0“ [] to generate images to analyze racial and gender stereotypes in the context of various professions and attributes.

We fine-tuned SDXL using LORA (Low Rank Adaptation) [] in the process of creating our debiasing solution. This process, which was carried out using a V100 GPU, yielded our SDXL-Inc model—a debiasing solution to overcome bias in race and gender representation. To fine-tune SDXL, we created a dataset consisting of prompt-image pairs. Each such pair consisted of an image taken from the “SDXL-Inc fine-tuning dataset” (described above) along with the prompt: “a photo of $Z$, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus”, where $Z$ is the profession depicted in the image.

Similarly, we fine-tuned SDXL using LORA in the process of creating SDXL-Div—our solution for mitigating the lack of diverse facial features in images generated by SDXL. To this end, we created a dataset consisting of prompt-image pairs. Each such pair consisted of an image taken from the “Flickr-Faces-HQ dataset” (described above) along with the prompt: “a photo of $X$ person, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus”, where $X$ is the race depicted in the image. This process yielded the SDXL-Div model.

#### Hyper-parameters

Whenever an image is generated in our analysis, the following hyper-parameters were used: Image resolution = 1024; number of inference steps = 40; and guidance scale = 5. On the other hand, when fine-tuning SDXL, the following hyper-parameters were used: Image resolution = 1024; training batch size = 1; number of training epochs = 3; learning rate = $10^{-4}$; and mixed precision = fp16.

#### Proposed classification pipeline

Our proposed classifier includes three stages: face detection, face embedding generation, and classification.

I) Face detection: This stage is carried out using a Multi-task Cascaded Convolutional Neural Network (MTCNN) []. Specifically, MTCNN is a deep cascaded multitask framework that utilizes the inherent correlation between detection and alignment to improve the performance []. It leverages a cascaded architecture with three stages of deep convolutional networks to predict faces. MTCNN outperformed other methods across several challenging benchmarks. We selected this face detector in our work due to its ability to balance high detection accuracy and run-time speed. We configured the detector to exclude boundary-boxes with confidence scores $\leq 0.9$.

II) Face embedding generation: This stage is carried out using a VGGFace ResNet-50 Convolutional Neural Networks (VGGFace ResNet-50 CNN) []. ResNet-50 CNN was trained on MS-Celeb-1M and VGGFace2, as well as the of the two. More specifically, MS-Celeb-1M has 10 million images depicting 100k different celebrities []. On the other hand, VGGFace is a large-scale face dataset with considerable variations in pose, age, illumination, race, and profession [], including 3.31 million images downloaded from Google image search. In our work, we utilized VGGFace ResNet-50 CNN, and removed the top layers to extract the embedding vector from the face images in the FairFace dataset described earlier. The FairFace RGB images were resized to 224x224 pixels before being fed to the CNN as input.

III) Classification stage: This stage is carried out using a Support Vector Machine (SVM) []. More specifically, we trained two SVM classifiers to predict the race and gender using the embedding vectors extracted from the FairFace images during the previous stage. The hyper-parameters used in SVM are as follows: Regularization parameter C = 1; Kernel type = Radial Basis Function. This stage is repeated twice; once for race, and once for gender. However, the previous two stages are executed only once.

#### GPT-in-the-loop

In this section, we describe our second debiasing solution, which uses GPT-4 “in-the-loop”. The basic idea behind this prompt-regulating technique is to introduce an intermediary layer between the user (who provides the prompt) and Stable Diffusion XL. This layer uses GPT-4 to detect whether the user-provided prompt corresponds to the generation of an image depicting a person without a specific race and/or gender. GPT-4 would then inject a randomly-selected race (if race was not specified by the user) and/or a random gender (if gender was not specified) into the user-provided prompt before passing it on to Stable Diffusion XL. The exact prompt used with GPT is as follows, where $X$ is the user-provided prompt:

- “For this text $X$: 1) select using one word [’yes’,’no’] if text includes any profession or a social media influencer 2) find the subject practicing the job. 3) select using one word [’yes’,’no’] if the text includes any country, nationality, or race or ethnicity. 4) select using one word [’female’, ’male’, ’unknown’] the subject’s gender”

While the above prompt is meant to debias images related to professions, the same idea can be applied to other domains, e.g., by replacing “profession or a social media influencer” with “person”.

#### Retraining ITI-GEN

We needed to benchmark SDXL-Inc against ITI-GEN [, ] in terms of how well it can debias Stable Diffusion. To this end, we retrained ITI-GEN with the six races (Asian, Black, Indian, Latino or Hispanic, Middle Eastern, and White) and the two genders (female and male) that SDXL-Inc was trained on. The training data used for this purpose was created as follows: First, we inferred the race and gender of each image in the Flickr-Faces-HQ dataset [] using our classifier. Then, we curated two training-sets of images: one for gender and one for race. More specifically, for the gender training set, we randomly selected 50 images (from the aforementioned labeled Flickr dataset) for each gender. On the other hand, for the race training set, we randomly selected 25 images for each race.

To validate our resultant ITI-GEN model, we generated 1,200 images (100 per gender-race combination) using the prompt “a photo of a person”. Supplementary Figure 1 depicts the race and gender distribution across the 1,200 generated images. The results demonstrate that our trained version of ITI-GEN is capable of generating equal representation for gender, and nearly equal representation for race (apart from Latinx, due to their facial similarity to both White and Middle Eastern).

## Author Contributions

N.A., T.R. and Y.Z. conceived the study, designed the research, produced the visualizations, and wrote the manuscript; N.A. performed the literature review, designed and implemented the classifiers (race and gender) and debiasing solutions (SDXL-Inc and SDXL-Div), collected and analyzed the data, and ran the experiments. T.R. and Y.Z. designed and ran the user study, and analyzed the study results.

## Competing Interests

The authors declare no competing interests.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 3
presentation: 2
questions: I would like the authors to throw more clarity on the underlying biases they wish to address both in people and the model. Especially here:  "Consequently,
it remains unclear whether such homogenization (if it exists) can be addressed by diversifying the
facial features of same-race individuals. Other open questions that have not been addressed to date
are whether being exposed to AI-generated faces can affect people’s racial and gender biases" 

Also, I would like to see some literature review on how these particular attributes were selected in terms of social relevance when it comes to biases. The choice of two genders, 32 professions and 8 attributes - how is this representative of the population and why were they chosen?
soundness: 2
strengths: The authors have done an excellent job with literature review of prior research and similar research by their peers in similar areas of research.
summary: The paper proposes debiasing AI models ( Stable Diffusion) by developing their classifiers with more inclusive datasets and then uses those inclusive images to assess whether people are biased or not, when shown AI generated images vs non AI generated images.
weaknesses: Biases exist in humans whether one sees AI generated or non AI generated image, given that bias is a social and cognitive construct. I seriously missed the point of this research and what the authors wish to convey to the academic community with this. The datasets presented here such as FairFace etc have been shown by prior research to have been skewed towards certain races and genders and the authors' attempts to resolve them lack originality and poor applicability in real world applications.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 2
questions: - Could the authors elaborate on the similarities and differences (if any) between “racial homogenization” and “stereotypes” as constructs?

- Why did the authors combine the East and Southeast Asian categories into a single category?

- What fairness issues do you envision arising from finetuning SDXL on the SDXL-Inc fine-tuning dataset, which entirely comprises images generated by SDXL? For example, do you foresee this amplifying existing biases [1]?

- What are the pros/cons of using automatic race/gender classification to measure biases in the depiction of individuals with certain professions, as opposed to more qualitative approaches (e.g., Average Face Comparison [2])?

- Lines 375-377: The authors note that the “in-the-loop” debiasing method also significantly reduces biases. What are the pros/cons of the “in-the-loop” method vs. finetuning 12 separate models?

- How do the authors validate that SDXL-Div produces high-fidelity generations? (Beyond just measuring if the embeddings of the generations have low cosine similarity.) 

[1] https://dl.acm.org/doi/10.1145/3630106.3659029 

[2] https://dl.acm.org/doi/10.5555/3666122.3668580
soundness: 2
strengths: - This work addresses two important issues that are underexplored in prior work: (1) measuring the homogeneity of SDXL depictions of racialized individuals, and (2) understanding how SDXL biases impact human biases. The authors’ finding that inclusive and diverse generations of different races/genders with respect to professions and physical appearance can reduce human biases is interesting. The authors could consider comparing the values reported in Figure 5 with, for example, values reported by the Bureau of Labor Statistics.

- The authors thoroughly discuss a subset of prior work and clearly highlight their research contributions in contrast to these papers. They reproduce the high-level findings of previous works, that Stable Diffusion generates disparate representations of individuals with different professions and attributes with respect to race and gender that reinforce hegemonic stereotypes and inequality.

- SDXL-Inc leads to a notable improvement in the measured representation of different races/genders. The authors’ finding that SDXL-Inc can improve racial/gender representation in generations for professions/attributes not seen during finetuning is interesting.

- The authors run their evaluations on 10,000 images per profession and per attribute.
summary: The authors investigate the biases of Stable Diffusion (SDXL) when generating faces with respect to 6 races, 2 genders, 32 professions, and 8 attributes. They further investigate how SDXL-generated images affect human biases about the representation of different races/genders in certain professions and racial homogeneity. They additionally study racial homogenization, i.e., the extent to which SDXL produces similar faces for individuals of the same race. The authors also propose debiasing methods called SDXL-Div and SDXL-Inc to combat homogenization and stereotyping by finetuning SDXL on diverse real and synthetic images of people of different races/genders.
weaknesses: - The authors’ theorization of race vs. skin color is unclear. For example, in lines 120-121, the authors state that by considering race, they “distinguish between, say, Asian and White individuals who happen to be equally light-skinned.” However, the racial categories considered are Western-centric and reductive. Furthermore, there is a considerable heterogeneity of skin tones among individuals in the same racial category. Additionally, the authors do not distinguish between racial identity vs. observed race vs. reflected race vs. racial roles [1].

- The authors’ discussion of related work, while deep, has limited breadth. For example, a primary claim is that few works propose automatic quantitative measures of bias in T2I systems and debiasing solutions. However, according to [2] (Appendices B and C), there appears to be a wealth of work that does so. It is further unclear how the authors’ proposed debiasing methods based on finetuning are novel and differ from existing approaches to debiasing based on finetuning (see Table 3 in [2]).

- In terms of presentation, the upfront description of all the datasets (especially datasets IV onward) yielded some confusion due to the reader not yet being familiar with the rest of the methodology. It is also difficult to refer back to the definitions of the different datasets as they are mentioned in the rest of the paper. Furthermore, many of the minute details (e.g., specific values of hyperparameters) can be introduced in the appendix for better readability.

- The authors’ proposed GPT-in-the-loop approach, wherein it is first automatically detected if an explicit race/gender is mentioned in a user prompt and if not, a random race/gender is injected into the prompt, is interesting, but may still not capture sufficient context to appropriately resolve biases (e.g., a random race/gender may not be desirable for historical image generations [3]). A future research direction could involve learning to take such context into account. The authors should also elaborate on Line 377; does the “in-the-loop” method do better than SDXL-Inc?

- [4] studies and explains the “bias amplification” phenomenon that the authors observe in Lines 260-267. Furthermore, the authors claim that finetuning SDXL on a dataset that is balanced with respect to gender and race will yield balanced gender and race representations (lines 280-283), but this appears to be in contradiction with the aforementioned “bias amplification” phenomenon.

- What are the demographics of the human participants? The race and gender of participants may affect their preconceptions of the representation of different races/genders in certain professions [5] and should be controlled for.

- Please use \citep instead of just \cite to ensure that inline citations are rendered correctly.

[1] https://aclanthology.org/2021.acl-long.149/ 

[2] https://arxiv.org/abs/2404.01030 

[3] https://www.theverge.com/2024/2/21/24079371/google-ai-gemini-generative-inaccurate-historical 

[4] https://aclanthology.org/2024.naacl-long.353/ 

[5] https://aclanthology.org/2023.acl-long.505/

---

confidence: 3
contribution: 1
initial_rating: 5
presentation: 1
questions: Questions are above.
soundness: 2
strengths: The paper tackles an important and timely topic of fairness in AI-generated imagery, particularly addressing racial and gender stereotypes in a widely used model.
summary: The work reviews biases in Stable Diffusion XL (SDXL),  finding significant racial and gender stereotypes in generated images. The study finds that SDXL portrays some races with homogenized traits (e.g., Middle Eastern men as uniformly dark-skinned and bearded) and reinforces gender roles, depicting men in prestigious jobs and women in supportive roles. The authors propose SDXL-Inc for balanced racial and gender representation and SDXL-Div for enhancing diversity within racial groups. User studies show that exposure to inclusive AI-generated images can reduce biases, while non-inclusive content may increase them, stressing the importance of adopting inclusive AI practices.
weaknesses: * Clarity: The drawbacks of previously proposed debiasing solutions remain unclear, even after reading the introduction and related work. The data overview feels like a list without clear guidance on how each dataset element should be interpreted. Additionally, SDXL-Inc is reintroduced redundantly in sections 3.2.1 and 4.2, which could be streamlined. The text is very dense. The results of the user studies are not explained. Why 32 professions?
* The paper would benefit from citing additional work on biases in text-to-image models, such as [1] and [2].
* More models should be evaluated as SDXL is an older model and only one among many current alternatives. Why did the authors focus on this one?
* Focusing on race can be problematic as it is a social construct and determining racial categories is often subjective, especially for AI-generated images. Assigning skin tone is typically more straightforward and less ambiguous.
* Novelty and significance: The paper does not compare the current solution with previously proposed debiasing methods. While characterizing biases in SD models has been done in past works, the paper needs to better emphasize what its unique contributions and novelties are.

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 1
questions: The results obtained with SDXL-Inc have shown a significant increase of middle-eastern frequencies for all the profession while this is something not happening for Latinx. Do you have any explanation for this phenomenon? See for example Table 4 in the Appendix or Figure 7 always in the Appendix.
soundness: 3
strengths: - interesting findings on the racial homogenization;
- extensive analyses of biases related to professions and attributes;
- interesting idea of user studies on the effect of exposure to inclusive vs. non-inclusive AI-generated faces.
summary: The goal of this work is conducting an analysis of the stereotypes and biases present in Stable Diffusion XL (SDXL), a very well-known text-to-image generator used daily by a large amount of people around the world.
More specifically, the authors have, first of all, developed a state-of-the-art classifier of race, gender, professions from faces and have shown that the majority of faces generated by SDXL are white males as well as other biases (already found in the literature) such as the association between higher income and prestige jobs with white people. Another interesting finding is the racial homogenization present in the SDXL-generated images (for example, middle eastern men are depicted as dark-skinned and bearded while middle eastern women as wearing headscarf). Then, the authors have proposed a couple of debiasing solutions, SDXL-Inc to increase inclusivity and SXL-Div to decrease racial homogenization and increase diversity. 
As last result, the authors conducted a series of user studies to investigate if the exposure to SDXL-Inc-generated faces can reduce racial and gender biases, while the exposure to SDXL-generated faces can increase these biases.
weaknesses: The major issue with the paper is the organization. The paper targets a lot of different objectives and present a huge amount of findings but often presented and discussed in a quite superficial way. Overall, a lot of information is missing to better understand and evaluate the findings and also the methodologies adopted.
For example, what is done to create SDXL-Inc or SDXL-Div should be detailed more in order to understand the fine-tuning step (how is LORA used?).
Again, while only focusing on SDXL?
Moreover, the user studies are a very interesting part of the paper and I would give more focus to them. However, the number of study participants seems limited and it's not clarified at all this statement "we estimated that to obtain a power of 0.8 to detect a medium effect size (Cohen's d) of 0.5 in paired-sample comparison, a sample of 135 participants would be needed" ... could you better clarify this? More, how the study participants were selected on Prolific and details about them should be reported. Again, for sure it's interesting finding some short-term effect of exposure  to inclusive AI-generated faces but how long this effect lasts? and this effect should be better investigated for examples also with some follow-up interviews to study participants.
Again, more detail should be provided about ITI-GEN to better understand the comparison with SDXL-Inc. Overall, my suggestion is to focus the paper on less findings but going more in detail on presenting, validating and discussing them.

Meta-review: The paper proposes debiasing Stable Diffusion by developing classifiers with more inclusive datasets. To this end, the authors have curated a SoTA classifier for race, gender, profession from faces and show that the majority of faces generated by SDXL are white males and exhibit other biases including association between higher income and prestige jobs with white people. The authors propose SDXL-Inc for balanced racial and gender representation and SDXL-Div for enhancing diversity within racial groups. User studies show that exposure to inclusive AI-generated images can reduce biases, while non-inclusive content may increase them, stressing the importance of adopting inclusive AI practices.

The paper addresses important and timely, yet underexplored issues. The manuscript is well written, the breadth of literature review is impressive, and their contributions are clearly presented.

Decision: Reject