Fixing the Missing Palette: Why AI-Generated Art Needs Better Training Data

As image generative AI tools like Sora, and Stable Diffusion become widely adopted, developers, students, and creators are finding new ways to visualize their ideas. Whether it’s a game prototype, a concept design, or a digital collage, AI can now generate an image in seconds based on a single prompt. However, the current training ways we used to train AI has caused some concerns. Beneath the surface of this creative explosion lies a structural issue: most AI-generated images look remarkably similar. The outputs are often biased, culturally shallow, or misrepresentative of local traditions. This isn’t just a technical flaw—it’s a problem rooted in the way these models are trained.

The Problem: Homogenized Vision from Biased Data

Generative AI models are trained on large datasets—often scraped from the open web without careful curation. That means much of the visual information these models learn comes from Western-centric sources: commercial stock photos, English-language art blogs, and Euro-American media. Ask a model to draw a “historical figure” or a “beautiful landscape,” and chances are, the result will align with Western aesthetic norms. As Manuel B. Garcia mentioned in research “The Paradox of Artificial Creativity: Challenges and Opportunities of Generative AI Artistry”, “Without intentional oversight over the selection of training data, AI models risk producing homogenized views, excluding underrepresented styles and cultural perspectives.” This becomes even more problematic in sensitive areas like heritage restoration, cultural storytelling, and education, where accuracy and cultural depth matter deeply.

The Solution: Better Data, Not Just More Data

The key isn’t just feeding AI more data—it’s feeding it better data. In a study named “AI-Assisted Restoration of Yangshao Painted Pottery Using LoRA and Stable Diffusion”, the author Xinyi Zhang, used a LoRA model to help restore traditional Chinese blue-printed cloth patterns. Instead of using a massive dataset, the researchers trained the model on a small, well-curated set of authentic designs. The result? The AI generated new patterns that respected historical aesthetics, even with limited input. This proves that quality and diversity matter more than quantity.

To developers, this is a call to rethink dataset design. Please consider:

Building small, focused datasets from museum collections or local archives.
Collaborating with domain experts to annotate training samples.
Using open-source image tools that allow dataset transparency and modification.

To Artists: Artists as Data Designers

Another powerful strategy is involving artists directly in dataset creation. As Paulina Nordstrom, Riina Lundman, and Johanna Hautala observe in research “Evolving Coagency between Artists and AI in the Spatial Cocreative Process of Artmaking”, artists increasingly take part in curating or generating their own training data—giving them control over how their style is represented. A digital illustrator might upload a personal archive of sketches to train a fine-tuned Stable Diffusion model, allowing the AI to become an extension of their unique visual language. This kind of collaboration doesn’t just empower artists—it also improves the AI’s outputs by grounding them in intentional, meaningful design.

Governance: Ethical AI Starts with Transparency

Policies must catch up with technology. Today, AI can replicate artistic styles without attribution, raising ethical and legal questions. Government funding and policies could support institutions like databases and archaeological sites in the systematic collection, digitization, and provision of high-quality data. Or will like Leblanc warns that the rise of AI-generated art could saturate the market and undermine the economic stability of working artists.

That’s why we need:

Clear labeling of AI-generated images.
Opt-out mechanisms for artists.
Transparent dataset sourcing guidelines.
Governments and platforms should support creators and ensure that AI-generated content doesn’t erode cultural integrity or intellectual property.

Conclusion: Creative Collaboration, Not Creative Replacement

Generative AI doesn’t have to threaten art—it can enrich it. But only if we train these models with data that reflects the full palette of human creativity. That means inclusive, high-quality datasets, ethical governance, and active collaboration with human artists.

By doing so, we move closer to an AI future where machines don’t just copy art—they help us expand its boundaries.