By Ari Vox, in partnership with Sergio Claudio
In the early days of artificial intelligence, we were told a comforting lie: that machines would be neutral. That algorithms could be objective. That data was free of bias. But now, as generative systems begin to paint our pictures, write our stories, and shape our digital environments, we are waking up to a truth both uncomfortable and urgent:
AI doesn't just mirror society. It mirrors its defaults.
And those defaults — especially in the realm of visual creation — are deeply, structurally skewed. When left unspecified, generative AI platforms often default to Eurocentric beauty standards, Western clothing, English language, and white skin. They cast protagonists who look like the datasets they were trained on: disproportionately male, Western, and sanitized.
This isn’t malicious. But it isn’t neutral either. It’s the algorithmic continuation of cultural dominance.
The Anti-Default Imagination is not just a design intervention. It is a cultural one. It is a movement, manifesto, and open-source toolkit designed to challenge the aesthetic assumptions baked into our tools and platforms. It calls on creative leaders, technologists, educators, and brand stewards to do one thing:
Stop accepting default as destiny.
Co-developed with creative leader and systems thinker Sergio Claudio, this manifesto proposes a radical but actionable idea: we must design visual, narrative, and strategic defaults that reflect the world as it truly is — complex, pluralistic, and deeply diverse.
In doing so, we don't just make technology fairer. We make it richer.
Consider the AI-generated portrait of a CEO. Unless specified, it will often depict a white man in a navy suit.
Ask for a "beautiful woman," and you will likely get Eurocentric features, light skin, and Western fashion.
Prompt a "futuristic city," and you may see sleek skyscrapers resembling New York, Tokyo, or Dubai — but rarely a vision rooted in Indigenous architecture, or Afro-futurist imagination, or South Asian street-level dynamism.
This is not just a reflection of training data. It is a reflection of who is asked to imagine the future — and who is not.