How AI Fashion Models Reflect Global Ethnicities
Fashion Technology
Jul 10, 2025
AI fashion models are revolutionizing representation in the industry, showcasing diverse ethnicities while highlighting ethical challenges.
AI fashion models are reshaping how the fashion industry addresses representation. These digital creations simulate human features, offering diversity in skin tones, body types, and identities, while also reducing costs for brands. By using advanced tools like GANs and 3D modeling, brands can create visuals that resonate with global audiences. However, challenges like biased datasets and superficial representation remain critical. Real-world examples, such as Levi Strauss & Co.'s AI model pilot and H&M's digital twins, highlight both the opportunities and complexities of using AI for diversity. To succeed, brands must prioritize ethical practices, ensure fair representation, and balance AI use with human creativity.
Fashion Brands Are Using AI Models For Diversity
Technology Behind Reflecting Global Ethnicities
Creating AI fashion models that represent a wide range of ethnicities requires sophisticated AI systems and diverse, high-quality data. By combining various advanced technologies, these systems produce lifelike models that respect cultural distinctions while aligning with commercial goals. Here’s a closer look at the innovations driving this progress.
AI Technologies That Enable Representation
The backbone of ethnically diverse AI fashion models lies in generative adversarial networks (GANs), machine learning algorithms, and 3D modeling tools. These technologies work together to create realistic models that showcase diverse skin tones, facial features, and body types.
For example, machine learning algorithms analyze extensive datasets to capture subtle differences in ethnic characteristics, such as bone structure, facial proportions, skin textures, and hair types. This ensures that the AI-generated models are both realistic and reflective of global diversity.
Meanwhile, 3D modeling technology allows designers to experiment with body proportions and simulate how fabrics interact with different skin tones. By fine-tuning elements like lighting, shadows, and color interactions, this technology ensures that garments look natural and appealing on models of all backgrounds.
Additionally, generative design systems contribute by suggesting color palettes and styles that resonate with specific cultural groups. These tools help brands create campaigns that connect meaningfully with diverse audiences, all while avoiding the logistical hurdles of traditional photography.
Why Diverse Training Data Matters
The accuracy of AI models in representing different ethnic groups depends heavily on the diversity of the training data. Without inclusive datasets, AI systems risk developing biases that misrepresent or exclude certain demographics.
Christine Marzano, founder of fashion-tech platform Bods, highlights this issue:
"The biggest challenge for AI representation is a lack of data on certain demographics, and less data means the AI is going to be less accurate."
This problem is evident in some commercial facial recognition systems, which often misclassify dark-skinned women while correctly identifying fair-skinned men. Building diverse datasets requires deliberate effort and significant investment, including sourcing images that represent a variety of ethnic groups, age ranges, and body types. Though costly and time-intensive, this process is crucial for creating fair and inclusive AI systems.
Companies like Lalaland.ai are tackling this challenge by collaborating with brands to develop AI models using licensed images from diverse communities. This approach ensures that the resulting image databases reflect a broader spectrum of human diversity.
Ranjan Roy, VP of strategy at Adore Me, underscores the importance of this work:
"The datasets that are going to drive business for the next five to 10 years are being built now, and the training sets we're using now are biassed or built by a very specific population. It's really exciting and important that brands can train image models with diverse models rather than using AI to replace diverse models."
Synthetic data can also help fill gaps in representation, but it must be carefully designed to avoid reinforcing existing biases. Combining authentic data with synthetic augmentation creates more balanced training sets, forming the foundation for the hyper-realistic and inclusive models discussed earlier.
Custom Models Through Digital Twins
Digital twins - digital replicas of real people - offer a practical way to ensure authentic representation while minimizing logistical challenges and costs. This technology allows brands to feature models from a variety of ethnic backgrounds without the need for extensive travel or traditional photo shoots.
H&M has embraced this approach, creating AI-generated digital twins of 30 real-life models for use in marketing and social media campaigns. These models retain ownership of their likenesses and are compensated for their digital appearances.
Jörgen Andersson, H&M’s chief creative officer, explains their vision:
"We are curious to explore how to showcase our fashion in new creative ways - and embrace the benefits of new technology - while staying true to our commitment to personal style."
Digital twins offer unique advantages over entirely synthetic models. They maintain a connection to real individuals, allowing for negotiation over representation and compensation. This ensures a more respectful and authentic portrayal of diverse ethnic groups. Moreover, models can earn passive income from their AI-rendered likenesses, expanding their reach globally without physical travel.
Bods takes personalization even further by creating avatars based on consumers’ exact measurements. This technology lets users virtually try on clothes, adjusting for factors like musculature, curviness, breast positioning, and skin tone. Such customization addresses the unique body proportions and fit preferences of different ethnic groups.
Impact on the Fashion Industry: Opportunities and Challenges
The rise of AI fashion models representing global ethnicities is shaking up the fashion world in big ways. On one hand, it’s opening doors for brands to connect with a broader range of audiences. On the other, it’s introducing some tricky challenges that need careful handling.
Opportunities for Diversity and Representation
AI technology is changing the game when it comes to diversity in fashion. By using AI models, brands can now showcase clothing on individuals of all ethnicities, body types, and age groups - something that traditional photography often struggles to achieve due to logistical hurdles.
Here’s what’s happening: AI is breaking down long-standing barriers in fashion campaigns. For example, in 2025, 32% of runway models are Black, Asian, Indigenous, or Latinx, and 38% of fashion models are based in non-Western regions. Additionally, 9% of campaign models have visible disabilities or neurodivergent traits. This shift is driven by both evolving consumer expectations and the practical capabilities of AI.
AI also cuts costs by eliminating the need for expensive international shoots. This allows brands to cater to regional and cultural preferences more easily. Take Levi Strauss & Co., for instance - they partnered with Lalaland.ai in 2023 to test AI-generated models that represented a wider range of body types and underrepresented demographics. This pilot program let customers see how clothing would look on models resembling their own body shapes and ethnic backgrounds. It also enabled the use of size filters to supplement traditional photoshoots.
Challenges in Representation and Accuracy
Despite its potential, AI in fashion isn’t without issues. One major hurdle is ensuring that AI models offer authentic and respectful representation. Because AI systems rely on training data, they can unintentionally mirror existing biases, undermining efforts to promote diversity.
A key concern is the possibility of superficial diversity - where AI is used to create the illusion of inclusivity without real commitment to diverse communities. Sometimes, AI even produces exaggerated or homogenized depictions of racial identity, which can reinforce harmful stereotypes rather than break them down.
Examples of these challenges are all too real. Back in 2015, Google faced backlash when its photo application mistakenly labeled a picture of two Black individuals as gorillas due to biased training data. More recently, model Nicole Harris encountered cultural misrepresentation when a bindi was added to her AI-generated headshot, despite her not being Hindu.
Sara Ziff, a former model and founder of the Model Alliance, highlights the issue:
"Fashion is exclusive, with limited opportunities for people of color to break in. I think the use of AI to distort racial representation and marginalize actual models of color reveals this troubling gap between the industry's declared intentions and their real actions."
Even Levi Strauss & Co. faced criticism after their AI pilot program. Addressing the backlash, the company clarified:
"We do not see this pilot as a means to advance diversity or as a substitute for the real action that must be taken to deliver on our diversity, equity and inclusion goals and it should not have been portrayed as such."
These challenges highlight the need for systems that balance diversity with authenticity, as explored below.
How AI Marketplaces Support Diversity
AI marketplaces like BetterStudio are stepping up to tackle these issues. These platforms offer tools that help brands create visuals celebrating cultural heritage and embracing a wide range of body types and ages.
Research by McKinsey & Company shows that inclusive brands often outperform those that aren’t. In fact, 70% of consumers say they prefer brands that highlight diversity in their marketing. This makes AI models that prioritize inclusivity not just ethically important but also good for business.
BetterStudio uses AI models and digital twin technology to create localized, diverse content. Their platform allows brands to generate product images featuring models of different ethnicities, body types, and gender expressions - all while maintaining authenticity .
Experts stress the importance of human oversight in these processes. Stefan Jakubowski, a fashion photographer, explains:
"AI might be doing more of the creative 'heavy lifting' but the ideas, the creative direction, the choices will always come from humans."
Ethics and Fashion Consultant Franki Tabor agrees:
"Fairness in AI is not just an option; it's the foundation of equitable technology for all. Accountability in AI (is) where innovation meets responsibility, ensuring technology serves humanity."
Platforms like BetterStudio show how AI can bridge the gap between technological advancements and cultural sensitivity, helping brands scale their diversity efforts while staying authentic and respectful to global audiences.
Case Studies and Real-World Applications
The fashion industry has embraced AI to showcase diverse representation, offering both promising examples and notable challenges. These real-world cases highlight how AI is being used to push boundaries and redefine inclusivity in marketing campaigns.
Examples of AI-Generated Models in Campaigns
In March 2023, Levi Strauss & Co. teamed up with Lalaland.ai to introduce AI-generated models on their e-commerce site. These models showcased a variety of sizes, skin tones, and ages, allowing shoppers to use size filters and view clothing on different body types. However, the initiative faced criticism, prompting Levi Strauss & Co. to clarify that this AI pilot was not intended as a replacement for genuine efforts toward diversity, equity, and inclusion goals.
Etro's Spring 2024 "Nowhere" Campaign took a more artistic route with AI. Collaborating with digital artist Silvia Badalotti, Creative Director Marco De Vincenzo created ethereal visuals of AI-generated models wearing the collection in surreal landscapes. The campaign sought to depict "a humanity that is both familiar and alien".
Meanwhile, Mango's Teen Line Campaign for its limited-edition Sunset Dream collection used generative AI to appeal to younger audiences in 2024. Similarly, Motorola's "Styled With Moto" Campaign featured AI-generated portraits of models from various ethnic backgrounds wearing a "Motorola Dress" inspired by the brand’s design identity.
Diverse Representation Initiatives
AI has also been a driving force behind initiatives that celebrate diversity in fashion. The Diigitals, for instance, created Shudu, a Black computer-generated model who has become a prominent AI influencer. Shudu represents a blend of digital creativity and real-world impact. Alexsandrah, who has modeled as Shudu for Vogue Australia, and writer Ama Badu, who developed Shudu's backstory, have contributed to making this project a trailblazer in the industry.
Alexsandrah reflected on the project’s legacy:
"It's something that even when we are no longer here, the future generations can look back at and be like, 'These are the pioneers.'"
In another groundbreaking effort, The Diigitals collaborated with Down Syndrome International (DSI) to create Kami, an AI-generated influencer designed with features associated with Down syndrome. This initiative highlights the importance of representation in the metaverse:
"The purpose of creating Kami was to celebrate and promote diversity within the metaverse, showcasing the incredible talents and capabilities of individuals with Down syndrome."
Microsoft Advertising has also contributed to inclusive representation by integrating with Shutterstock to offer "people filters." These filters allow advertisers to search for images based on attributes like gender, ethnicity, age, and group size. Microsoft’s research shows that inclusive visuals can enhance brand trust, loyalty, and engagement, leading to higher click-through rates and stronger customer connections.
BetterStudio's Role in Scaling Diversity
Platforms like BetterStudio are stepping up to meet the growing need for diverse AI fashion models. BetterStudio provides tools for brands to create authentic, culturally aware content at scale. By enabling brands to generate product images with models of various ethnicities, body types, and gender expressions, the platform bridges the gap between inclusivity and efficiency.
BetterStudio’s technology also supports localized content creation, ensuring cultural nuances are respected while delivering the high-quality visuals today’s consumers expect. This aligns with the broader trend toward inclusive marketing, where diverse representation isn’t just ethically important - it’s also a smart business move. With the global influencer market projected to surpass $32.55 billion by 2025, the value of authentic, diverse content that resonates with audiences worldwide continues to grow.
Ethical Considerations and Best Practices
As the article highlights the importance of inclusivity and respect in AI-driven models, ethical practices take center stage. The rise of AI fashion models brings forth unique challenges that brands must approach with care and responsibility.
Ensuring Respectful Representation
True representation goes beyond just visual accuracy - it requires a deep understanding of cultural nuances, respect, and the inclusion of authentic perspectives. Unfortunately, many AI systems today are trained on datasets that heavily favor Eurocentric beauty standards, often failing to accurately showcase diverse ethnic features.
To address this, brands should actively engage with diverse communities to ensure cultural nuances are represented accurately, avoiding stereotypes or mischaracterizations. This approach is not just theoretical; it’s deeply personal for some. Michael Musandu, CEO and founder of Lalaland.ai, shared his motivation for ethical AI development:
"As a boy growing up in Zimbabwe, I rarely saw anyone who looked like me featured in photos, advertisements or the runway. This deeply impacted me and was a motivating force in my desire to utilize technology - ethically and responsibly - to drive real and sustainable change in representation."
A key step is sourcing diverse training data to prevent the reinforcement of harmful stereotypes. Brands must also design annotation systems that respect cultural differences and regularly audit their AI models by testing them with a broad range of user groups. These practices naturally lead to larger questions about transparency and ownership in the realm of AI.
Addressing Ethical Concerns in AI Modeling
The ethical implications of AI fashion modeling are complex and demand thoughtful action. Consent and intellectual property rights are at the forefront, especially when creating digital replicas or using characteristics from real individuals. Brands must secure explicit permission when using someone’s likeness and ensure that copyrighted or unauthorized content is avoided entirely.
Another pressing concern is the potential for AI to marginalize human models, with digital alternatives being used solely as a cost-cutting measure. Dale Noelle, founder of True Model Management, underscores the importance of empowering human models:
"Empowering diverse models by giving them the means and protection to capitalize on their digital twins through licensing agreements is essential."
Transparency is another critical factor. Brands should clearly disclose when AI-generated models are used in campaigns. This can be achieved by labeling digital models with hashtags like #AIGenerated or #VirtualInfluencer across social media and marketing materials. Additionally, AI avatars should not be given fake backstories that could mislead consumers into believing they represent real-life experiences.
Best Practices for Brands Using AI Models
To navigate these ethical challenges, brands must adopt thoughtful and proactive strategies. Ethical AI usage isn’t just about technology - it requires a strong commitment to diversity, equity, and inclusion (DE&I). Involving DE&I teams early in the process ensures that AI innovations are viewed through an equity lens, with potential impacts on underrepresented communities considered from the outset.
Building diverse development teams is another essential step. Having individuals from varied backgrounds involved can help uncover biases in datasets and create more inclusive digital personas. As Ranjan Roy, VP of strategy at Adore Me, explains:
"The datasets that are going to drive business for the next five to 10 years are being built now, and the training sets we're using now are biased or built by a very specific population. It's really exciting and important that brands can train image models with diverse models rather than using AI to replace diverse models."
Consumer participation in the development process is equally vital. Actively seeking feedback from audiences ensures alignment with their expectations. Ramona Dunlap, assistant professor at the Fashion Institute of Technology, warns of the risks of ignoring this step:
"That leads to the company having to spend the money twice because they've gone out there and invested in something that their audience doesn't want at all."
Generative AI can be used to create test campaigns, allowing brands to gather insights from both consumers and employees before launching full-scale initiatives. Striking a balance between AI and human creativity is essential - AI avatars should enhance human ingenuity, not replace it. Investing in human influencers alongside AI models helps preserve genuine connections with audiences.
The Future of AI Fashion Models and Global Representation
The fashion industry is undergoing a transformation, rethinking how brands approach diversity and representation. According to McKinsey, generative AI could contribute up to $275 billion to operating profits in the fashion, apparel, and luxury sectors by 2028. This shift highlights the growing role of AI in fostering inclusivity, with 73% of fashion executives planning to prioritize generative AI this year to support creative processes.
One key trend shaping this future is hyper-personalization. AI now allows brands to tailor experiences to individual preferences while respecting cultural nuances. With 73% of shoppers expecting brands to understand their tastes and preferences, AI models offer a practical solution. They can present products to diverse audiences without the logistical challenges of traditional photoshoots.
AI is also making strides in addressing sustainability. By optimizing garment patterns, AI helps reduce the staggering 186 billion pounds of textile waste generated each year. This not only benefits the environment but also enhances diversity in visual marketing, positioning AI as a tool for both inclusivity and eco-conscious innovation.
Another exciting development is how AI is breaking down barriers for underrepresented designers. By providing accessible tools, AI empowers aspiring creators from diverse backgrounds to enter an industry that has historically been exclusive. This accessibility could also help shift the demographics of the AI workforce, which is currently 91.88% male, opening doors for broader participation.
The concept of digital twins is another frontier. These digital representations allow models to maintain ownership and control over how their likeness is used, ensuring that technological advancements don’t jeopardize livelihoods - especially for models from underrepresented communities who have faced systemic challenges in the industry.
Looking ahead, the future of fashion lies in blending AI efficiency with human creativity. Successful brands are adopting hybrid approaches, combining the scalability of AI with the authenticity of human models. This strategy meets consumer demand for genuine connections while leveraging AI to produce diverse and inclusive content. As Musandu points out, human models remain crucial for fostering authentic consumer relationships.
For brands to thrive in this evolving landscape, investing in diverse datasets, ethical practices, and transparency is essential. Companies that embrace these principles while staying committed to authentic human representation will lead the way in creating a more inclusive and accessible global fashion industry.
For those ready to explore AI-powered diversity initiatives, platforms like BetterStudio offer tools that merge AI model generation with real model marketplaces. These solutions enable brands to scale inclusive content creation while supporting both technological progress and human talent.
FAQs
How do AI fashion models promote fair representation of diverse ethnicities in the fashion industry?
AI fashion models aim to promote fair representation by relying on training data that is purposefully diverse and balanced. Developers carefully curate datasets to include a broad spectrum of ethnicities, skin tones, and facial features, ensuring a more inclusive foundation. On top of this, advanced algorithms are fine-tuned to detect and address any imbalances, striving for more equitable outcomes.
There's also a strong push in AI research to prioritize transparency and accountability. These efforts are key to reducing bias and ensuring fairness. By embracing inclusivity, AI models give the fashion industry a chance to better mirror the diversity of its global audience. This approach opens the door for campaigns and content that feel more genuine and representative of the world we live in.
What ethical concerns should brands address when using AI fashion models?
When integrating AI-generated fashion models, brands must navigate several ethical challenges to maintain responsible practices. One of the most pressing concerns revolves around privacy and consent. Using a model's likeness without explicit permission can violate their rights, making it essential for brands to secure clear and informed consent.
Another critical issue is bias in AI training data. If the data used to develop these models lacks diversity, it can unintentionally perpetuate stereotypes or marginalize certain ethnic groups. To counter this, brands should focus on building datasets that reflect a wide range of ethnicities, body types, and identities, ensuring representation across the board.
By addressing these challenges head-on, companies can embrace AI in a way that upholds ethical standards while fostering fairness and inclusivity in the fashion industry.
What’s the difference between digital twins and fully synthetic AI models in terms of representation and authenticity?
Digital twins are incredibly detailed virtual versions of real people, crafted using actual human data. This means they closely resemble the physical appearance and unique traits of the individuals they represent. On the other hand, fully synthetic AI models are entirely computer-generated, built to showcase a variety of features and styles without being linked to any specific person.
Digital twins shine when it comes to delivering highly personalized and realistic representations. Meanwhile, synthetic AI models prioritize adaptability and creative freedom, offering a broader range of possibilities. That said, synthetic models can sometimes feel less genuine since they aren't rooted in a real-life counterpart. Both approaches bring their own strengths to the table, whether the goal is to create relatable, human-centered content or to experiment with imaginative and diverse designs.