Study: AI Model Diversity in Fashion Campaigns
Fashion Technology
Jul 5, 2025
Explore how AI-generated models are enhancing diversity in fashion campaigns while addressing ethical challenges and measuring success.
AI-generated models are reshaping fashion campaigns by offering representation across various demographics - skin tones, body shapes, ages, and abilities. This approach addresses gaps in traditional fashion representation while cutting costs by up to 40% (McKinsey, 2024). Tools like BetterStudio allow brands to create diverse, customizable visuals, making campaigns more relatable and cost-efficient. However, challenges like bias, tokenism, and transparency remain critical. Brands must measure diversity using data-driven metrics and adopt ethical practices to ensure trust and authenticity in their campaigns. The future of fashion lies in balancing AI innovation with responsible representation.
Fashion Brands Are Using AI Models For Diversity
Key Metrics for Measuring Diversity in AI Fashion Content
To create truly inclusive AI-driven fashion campaigns, brands need more than just good intentions - they need measurable ways to evaluate their efforts. The challenge is turning concepts like inclusivity and representation into actionable data points that guide decisions and improve outcomes. Let’s explore the metrics and tools that help brands take a data-driven approach to diversity.
Diversity Metrics in AI-Generated Models
Measuring diversity in AI-generated fashion content starts with understanding demographic representation. This includes tracking factors like race and ethnicity, body shape and size, gender beyond the binary, age across life stages, disability inclusion, and representation of various cultural identities. Things get even more intricate when considering intersectionality - how well campaigns reflect individuals belonging to multiple underrepresented groups.
AI-Powered Tools for Diversity Analysis
Once brands define their diversity metrics, modern AI tools help quantify these variables. For example, computer vision models can analyze marketing visuals for skin tones, body shapes, ages, and facial features, while natural language processing (NLP) tools scan product descriptions and ads for gender-coded language or cultural insensitivity. These technologies can process thousands of images and texts, breaking down representation across categories with remarkable detail.
Microsoft Advertising, for instance, offers "people filters" that allow advertisers to search for images based on attributes like ethnicity, gender, age, and group size. This gives brands the ability to choose visuals that better reflect their audiences.
"Advertisers who use inclusive and representative visuals saw higher click-through rates and stronger customer resonance. Microsoft encourages the use of realistic, diverse imagery that reflects the identities of its audiences, ultimately supporting better campaign outcomes and a faster time to market." - Microsoft Advertising
Some platforms even provide DEI (Diversity, Equity & Inclusion) scores for campaigns, benchmarking them against brand objectives or industry norms. These scores simplify tracking progress over time and identifying areas where improvement is needed.
Benchmarking Campaign Diversity
Metrics are just the beginning - benchmarking adds valuable context. To set meaningful benchmarks, brands need to compare their campaigns not only against their own goals but also against broader industry standards and societal demographics. This starts with establishing a baseline: where does the brand currently stand in terms of diversity in its AI-generated content?
Brands that prioritize inclusivity often see better performance, both ethically and financially. However, challenges remain. The British Fashion Council's 2024 DEI report highlights that while models in ads and on runways are becoming more ethnically diverse, only 9% of executive teams in the industry are people of color. Even more concerning, a 2023 study revealed that three-quarters of organizations are not actively working to reduce bias in their AI models. This neglect risks perpetuating representation gaps, even when brands aim for diversity.
Effective benchmarking requires consistent use of metrics across campaigns and comparisons to multiple standards, such as industry averages, census data for target markets, and internal diversity objectives. Regular audits ensure brands make genuine progress over time.
"Fairness and bias mitigation in AI are not just technical challenges but ethical imperatives. Ensuring that AI systems operate equitably is essential for building trust in AI technologies and harnessing their potential for positive societal impact." - Franki Tabor, AI ethics and fashion consultant
Benchmarking is not a one-time task - it’s an ongoing process. The most successful brands treat it as a continuous effort, using data insights to refine strategies and ensure their AI-generated content authentically represents diverse audiences.
How AI-Driven Diversity Affects Campaign Success
Multicultural marketing campaigns tend to perform better, achieving 70% higher engagement rates than general market campaigns. When brands leverage AI to craft inclusive content, they often see measurable boosts in key performance indicators, such as click-through rates and customer loyalty. These gains not only enhance campaign outcomes but also lay the groundwork for stronger consumer trust.
Consumer Perception and Brand Trust
AI's role in promoting diversity significantly impacts how consumers perceive brands. Studies show that when consumers believe a brand authentically incorporates AI-generated diversity, they feel a stronger sense of belonging and experience reduced social identity threats. This connection fosters trust, which directly influences business success.
Transparency also plays a pivotal role. According to research, 72% of consumers are more likely to trust a company when recommendations are highly relevant to their needs. On the flip side, 75% of organizations acknowledge that a lack of AI transparency could lead to higher churn rates. Brands that not only embrace diverse AI representation but also openly share their methods gain a clear competitive edge.
Campaign Performance Metrics
The benefits of diverse AI-driven campaigns are evident across various performance metrics. For instance, AI-generated ads tailored to user preferences achieve 25% higher interaction rates compared to traditional ads. Moreover, companies that use advanced digital tools and proprietary data to personalize customer experiences report revenue growth of 6% to 10%.
Personalization powered by AI can also significantly enhance marketing ROI, with studies indicating a potential boost of up to 30% in ROI and improved customer engagement rates. Loyalty metrics tell a similar story: 49% of consumers are more likely to return to businesses offering personalized recommendations. In the fashion industry, for example, brands that use AI to reflect diverse body types, ethnicities, and styles foster deeper connections, leading to repeat purchases and stronger brand advocacy.
Case Studies in AI-Driven Diversity
Real-world examples highlight the tangible impact of diverse AI-driven campaigns. Take Zalando, for instance. The company introduced the Algorithmic Fashion Companion (AFC), a GenAI-powered tool that curates personalized outfit recommendations based on user preferences. The results? A 40% increase in average basket size and an 87% growth in adjusted EBIT during Q2 2023.
This example underscores how AI technologies can not only analyze individual preferences but also ensure campaigns resonate with diverse audiences. By aligning their strategies with diversity metrics, brands like Zalando strengthen customer relationships, unlock new market opportunities, and build the loyalty essential for long-term growth. Fashion brands that consistently prioritize inclusivity through AI are paving the way for deeper audience connections and sustained success.
Challenges and Ethics of AI Model Diversity
As brands incorporate AI into their efforts to promote diversity, they face ethical dilemmas that could undermine their intentions. While AI can contribute to more inclusive representation, it also introduces risks that may harm authenticity and erode trust if not carefully managed.
Risks of Tokenism and Surface-Level Diversity
One major challenge is tokenism - using AI-generated models as a superficial gesture toward inclusion rather than making meaningful changes. Over-reliance on AI for diversity can result in shallow efforts that reinforce stereotypes instead of breaking them down. This happens because AI systems often rely on biased datasets, which can replicate and even amplify historical patterns of exclusion and prejudice.
The numbers paint a stark picture: only 14% of white men believe the industry lacks diversity, compared to 54% of women of color and 48% of people with disabilities. Meanwhile, a 2023 study found that three-quarters of organizations fail to address bias in their AI systems, and over 91.88% of the AI workforce is male.
"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." - Franki Tabor, Ethics and Fashion Consultant
Another issue is the risk of cultural appropriation. Without incorporating diverse, real-world perspectives, AI-generated content can unintentionally exploit cultural elements in ways that lack understanding and respect. These risks highlight the importance of transparency, which is explored further in the next section.
Transparency and Authenticity Concerns
AI-generated models bring up significant concerns about transparency and trust. Consumers are increasingly wary of whether the images they see are authentic or digitally created. This skepticism has been fueled by incidents like Levi Strauss & Co.’s partnership with Lalaland.ai in March 2023. While the initiative aimed to create diverse AI models, critics questioned whether it genuinely promoted inclusivity or simply manufactured it for appearances.
To address these concerns, brands should openly disclose their use of AI in creating digital models and ensure that these visuals accurately represent real-world products. Failing to do so risks alienating consumers and damaging credibility, underscoring the pressing need for ethical guidelines.
Regulatory and Ethical Gaps
The rapid development of AI has outpaced the creation of comprehensive regulations. While 73% of fashion executives plan to integrate generative AI into their creative processes, regulatory oversight remains limited. For instance, the EU Artificial Intelligence Act requires transparency in AI-generated content, but many regions still lack specific guidelines for AI's role in fashion.
Intellectual property rights add another layer of complexity. The Model Alliance’s New York State Fashion Workers Act seeks to prevent unauthorized creation or manipulation of digital replicas of models, addressing concerns about consent and model rights. Beyond individual rights, algorithmic bias continues to perpetuate stereotypes around race, gender, body type, and socioeconomic status. Additionally, AI adoption could lead to job losses among design assistants, pattern makers, and textile workers.
"We believe human models will continue to play a vital role in the fashion industry, establishing genuine connections with consumers; our technology aims to support this. And, yes - we need more of them to come from underrepresented groups if fashion companies are serious about inclusion efforts." - Michael Musandu, CEO and founder of Lalaland.ai
To navigate these challenges, brands must implement ethical frameworks that emphasize transparency, fairness, and compliance. This includes using diverse datasets to minimize bias, setting clear standards for digital model creation, and engaging with communities to ensure respectful representation.
Practical Uses and Future Trends in AI Model Diversity
Fashion brands are increasingly turning to AI-driven solutions to embrace diversity in their content creation processes. What once seemed experimental has now become a cornerstone for producing inclusive, high-quality visuals at scale. These advancements not only address the need for representation but also pave the way for scalable solutions and future developments in the industry.
Current Applications of AI Diversity
AI is reshaping how brands approach representation in their marketing and product visuals. By leveraging AI, companies can now create content that features models of various ethnicities, body types, and gender expressions, making their offerings more inclusive. For instance, brands can showcase their products on a wide range of body types, ensuring that customers feel seen and represented.
Virtual photoshoots have emerged as a game-changer for retailers. Take ASOS, for example - they utilized AI to conduct virtual photoshoots where digital versions of clothing were mapped onto real-life models. This approach not only reduces logistical challenges but also enhances the creative possibilities for campaigns. AI is also transforming the shopping experience by offering personalized style recommendations that break away from traditional gender labels, a crucial shift as 73% of shoppers now expect brands to align with their unique preferences and tastes. Additionally, AI contributes to adaptive design by suggesting features that cater to individuals with disabilities, broadening inclusivity and reaching untapped markets.
In July 2024, Mango took a bold step by launching an advertising campaign featuring AI-generated avatars to promote a limited-edition collection for teenage girls. Meanwhile, AI-powered tools like virtual try-ons and augmented reality features are making shopping more interactive and accessible. These tools allow customers to see how products will look on diverse body types, improving satisfaction and reducing uncertainty.
BetterStudio's Features for Scalable Diversity
BetterStudio is at the forefront of helping brands scale their diversity efforts through advanced AI tools. Its bulk editing capabilities simplify the process of creating diverse campaign variations, saving time while ensuring inclusivity.
One standout feature is the platform's ability to create custom AI models. Instead of relying on generic AI-generated faces, brands can design models that truly reflect their target demographics and align with their brand identity. This ensures both authenticity and consistency in campaigns.
For larger enterprises, BetterStudio offers API integration, enabling seamless incorporation of diverse AI-generated models into existing workflows. The platform also emphasizes transparency by encouraging brands to disclose when digital models are used in campaigns. This openness helps build consumer trust, a critical factor as AI tools become more sophisticated.
Future Trends in AI and Representation
The next generation of AI tools will focus on hyper-personalization, allowing brands to go beyond basic demographics and cater to individual preferences and cultural subtleties. By analyzing massive datasets, AI will create tailored style profiles, enhancing the shopping experience and deepening customer engagement.
AI's ability to generate lifelike images is advancing rapidly. These photorealistic visuals are being used in campaigns, digital art, and immersive virtual reality experiences. As this technology evolves, transparency about AI usage will become even more important to maintain consumer trust.
Another emerging trend is the use of diversity auditing tools. These AI-driven systems evaluate visual and written content for representation, helping brands measure their inclusivity efforts and identify areas for improvement.
Microsoft's planned 2025 expansion of its Shutterstock integration offers a glimpse into the future. This integration will give advertisers access to over 360 million royalty-free images and allow filtering by attributes like gender, ethnicity, and age. Research shows that authentic representation not only builds trust but also boosts loyalty and purchase intent.
AI is also playing a role in circular fashion platforms, blending sustainability with diversity. These platforms use AI for tasks like garment wear detection, automated categorization, and dynamic pricing. By doing so, they enable brands to create diverse content while supporting eco-friendly practices.
The integration of AI with augmented and virtual reality is set to revolutionize retail. Imagine virtual stores where customers can view products modeled on bodies that reflect their own.
As AI continues to evolve, ethical considerations will take center stage. Brands will need to address challenges like bias and authenticity by establishing clear ethical guidelines and safeguards. Measures to prevent misuse, such as deepfake creation, will be critical to ensure that AI remains a tool for creativity and inclusivity while upholding legal and ethical standards .
The industry is also moving toward hybrid campaigns that combine AI-generated content with human models and influencers. This approach maintains the efficiency of digital tools while preserving the human connection that resonates with consumers.
Conclusion: The Path Forward for AI Model Diversity in Fashion
A recent campaign using diverse AI-generated models saw a 30% boost in online engagement and a 20% increase in sales. While these results are encouraging, they stand in stark contrast to the ongoing lack of diversity in leadership and AI development. Currently, only 9% of executive teams are people of color, and 91.88% of the AI workforce is male. These numbers highlight a systemic issue that technology alone cannot solve.
"When we say supplement, we mean the AI-generated models can be used in conjunction with human models to potentially expand the number of models per product... enabling us to create a more personal and inclusive shopping experience." - Levi's spokesperson
To truly advance AI diversity, brands must go beyond surface-level efforts. Shereen Daniels, managing director of HR Rewired, emphasizes the risks of using AI as a shortcut for representation:
"If you're a brand that already has challenges around representation and racial equity, tack [AI] on is always going to look problematic, like you're trying to take a shortcut to 'look better'."
This highlights the need for ethical, transparent practices. Genuine progress requires involving diversity and inclusion teams from the start, evaluating the impact of AI through an equity lens, and being upfront about how the technology is used. The aim is not to replace human models but to expand representation while staying authentic.
BetterStudio offers a practical approach to scalable AI diversity, combining custom model creation with transparent methods. Their API integration and bulk editing tools make it possible to scale diversity efforts without compromising quality or sincerity.
Brands that approach AI diversity ethically will not only build trust but also expand their market reach. By prioritizing inclusive datasets, rigorous bias testing, and clear communication, companies can foster stronger consumer relationships and tap into broader audiences. The technology to achieve this exists - the real challenge is using it responsibly.
As the fashion industry continues to evolve, the most forward-thinking brands will treat AI as a tool to enhance human diversity, not replace it. Moving forward requires addressing systemic inequities, using technology thoughtfully, and committing to authentic representation that reflects the richness of human experience.
FAQs
How can brands use AI-generated models to promote genuine diversity in fashion campaigns without appearing superficial?
To genuinely reflect diversity in AI-generated models, brands need to prioritize thoughtful and inclusive design. This means crafting prompts that embrace a wide range of diversity - covering ethnicity, age, body shapes, and cultural backgrounds - rather than settling for shallow or superficial representation.
Brands can take it further by auditing their datasets to identify and address biases, involving teams with varied perspectives in the creative process, and aligning AI-generated content with tangible diversity efforts. These actions help produce campaigns that feel inclusive and impactful, steering clear of tokenism and fostering real connection.
What metrics can fashion brands use to evaluate the success of AI-driven diversity efforts in their campaigns?
To evaluate the impact of AI-driven diversity efforts in fashion campaigns, brands can focus on tracking metrics like engagement rates, click-through rates, and conversion rates. These numbers reveal how well diverse imagery connects with audiences.
Brands can also establish diversity benchmarks for AI-generated models, ensuring representation spans across age, ethnicity, body shapes, and other characteristics. Using real-time dashboards to monitor customer sentiment and spot design trends can offer additional insights into how well diversity-focused campaigns are performing.
How can fashion brands ensure AI systems promote diversity and fairness in their campaigns?
Fashion brands have a real opportunity to embrace inclusivity in their AI-driven campaigns by using diverse and representative datasets to train their AI models. By doing so, they can minimize biases and create content that captures a wider array of identities and perspectives.
On top of that, brands should stick to ethical practices by routinely reviewing AI outputs to ensure fairness and working closely with teams that bring a variety of backgrounds to the table. This not only adds credibility but also ensures that AI-generated fashion content aligns with today’s values of inclusivity and representation.