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Civilisational AI: Preserving Culture and Diversity to Enrich Our Future With the Past

Being Human for a Better Tomorrow in the Age of AI

  • June 13, 2025
  • 49th edition
  • Vol. 2
Civilisational AI Preserving Culture and Diversity to Enrich Our Future With the Past

We Are Not Just Biological DNA; We Are a Composite With a Civilisational DNA

Every minute, 250 humans are born worldwide. Each will develop unique perspectives shaped by their cultural context—their language, traditions, and local knowledge systems. This diversity of human experience is our species’ greatest evolutionary advantage.

Human creativity and innovation are not universal monoliths but expressions of “Civilisational DNA”—the accumulated knowledge, language, values, and living practices inherited through our civilisational context. This diversity is humanity’s evolutionary advantage.

There is a strong theoretical basis for what we intuitively know. Proposed by anthropologist Joseph Henrich and others, the theory of Cultural Evolution and the “Collective Brain” argues that human success stems not from individual intelligence, but from our ability to learn from others, accumulate knowledge across generations, and build a shared cultural toolkit. Our species’ evolutionary advantage isn’t our individual brains, but our “collective brain” built from diverse, accumulated cultural knowledge.

As Henrich noted in his book, “The Secret of Our Success,” socially transmitted information (culture) is our species’ central adaptation. Innovation is often the result of recombining existing cultural elements in new ways, meaning a more diverse pool of cultural knowledge leads to more powerful innovation.

AI & Cultural Preservation: Safeguarding Our Heritage

For the first time, AI gives us the tools to safeguard and revitalise our collective civilisational DNA at an unprecedented scale. Sadly, for example, as per UNESCO, “A language disappears every two weeks; 40% of the world’s 7,000 languages are already endangered.”

Language (The Carrier of Culture)

What AI Can Do: Document and analyze grammar from limited data, create pedagogical tools, generate new learning materials, and preserve phonetic nuances.

SanskritShala: A neural Sanskrit toolkit with a web interface designed specifically for teaching and annotation purposes, serving as a perfect example of AI creating pedagogical resources.​

AI for Indigenous Languages: Researchers at institutions like Dartmouth are using AI to analyze Indigenous languages to create digital dictionaries and teaching tools from sparse data, providing a vital boost to language preservation efforts.

Art - The Visual Record

What AI Can Do: Restore damaged paintings, reconstruct lost artworks, and enhance our understanding of historical art techniques.

SINTA Project: This initiative aims to develop AI to analyze the “style” of historical artworks, helping to attribute unsigned works and understand artistic evolution by creating a “common language” to describe stylistic elements.

Recreating Klimt’s Lost Paintings: Using AI, Google Arts & Culture helped restore the color to three Gustav Klimt masterpieces destroyed in 1945, using black-and-white photos and analysis of his other works.

Art - Literature & Historical Texts (The Written Word)

What AI Can Do: Transcribe handwritten documents at scale, decipher damaged ancient texts, and make vast historical archives searchable.Example

The “Venice Time Machine” project uses AI to digitize 80 kilometers of Venetian archives spanning 1,000 years, creating a massive semantic network of historical data that allows historians to trace family histories, trade routes, and disease outbreaks in ways previously unimaginable.

Art - Music, Dance, Traditions

What AI Can Do: Analyze and archive folk music, capture the kinematics of traditional dance, and preserve oral histories. Examples:

Dance: The MIT Media Lab team digitised 59 canonical Thai “Mae Bot Yai” poses as interactive 3-D models, then built a generative algorithm that lets dancers remix those moves with virtual AI partners—essentially turning preservation into a springboard for new choreography.

Cultural Traditions: Peer-reviewed evidence that AI can recognise and teach nuanced movements from six Chinese minority dances (Miao, Dai, Tibetan, Uygur, Mongolian, Yi) with >95 % accuracy—exactly the “motion-capture + AI” combo that keeps tacit knowledge alive

Critical Tensions & Debates

Yet, for all its power to preserve, AI carries an equal, opposite risk. The technology is a double-edged sword, forcing us to confront critical tensions.

1. The Double-Edged Sword: Homogenization vs. Hybridization

AI presents us with a fundamental paradox. The same technology capable of preserving our cultural heritage at unprecedented scale also risks creating unprecedented homogenization. Understanding this tension—and choosing our path deliberately—may be one of the most consequential decisions of our technological age.

While there are promising steps in the right direction—initiatives like Google’s Monk Skin Tone Scale and Meta’s Voicebox show AI can be a tool for inclusion—we are simultaneously challenged by a powerful counter-current: the drift towards cultural homogenisation.

This risk manifests in several ways:

  1. Algorithmic Bias: The most powerful AI models are trained on datasets overwhelmingly dominated by Western, English-language sources. This inherently skews their worldview, creating a subtle but pervasive bias that can marginalize other cultural perspectives.

  2. The “Global Aesthetic”: This data imbalance often results in AI-generated content that, while technically proficient, has a generic “global aesthetic” that lacks the distinct character and nuance of local art forms.

  3. The Rise of Homogenic Advisory: Perhaps most concerning is the growth of homogenic AI in critical advisory roles. When an AI provides standardized advice on healthcare, wellness, or financial planning, it ignores the vast differences in cultural values and societal norms. The danger lies in an AI that ignores this deep context, offering one-size-fits-all advice that isn’t just unhelpful, but potentially harmful.

2. Ownership of Cultural IP (As Training and As Output)

As we explored in our 41st edition on ‘AI and Art,’ the use of existing works to train AI raises profound questions about fair compensation. This tension deepens when the source material is a community’s collective cultural heritage. The legal landscape is deeply divided:

  • United States: The U.S. is navigating this through legal battles. Cases like Thomson Reuters v. ROSS Intelligence and NYT v. OpenAI/Microsoft challenge the use of copyrighted content for training.
  • United Kingdom & Japan: Other jurisdictions have taken a more permissive stance. The UK allows the use of copyrighted works for training, while Japan's law is even broader, permitting it for information analysis to foster innovation.

Beyond training data, a parallel debate is emerging around the ownership of AI’s output. The core issue, as explored in comparative legal analyses, is the principle of “human authorship.”

Legal systems are grappling with whether AI is merely a sophisticated tool (like a camera), where the human user is the author, or if it can be an author itself. The current stance of the U.S. Copyright Office is that a work must contain sufficient human creativity to be copyrightable. In the Zarya of the Dawn case, it granted copyright for human-created elements (text, arrangement, image selection) while denying it for purely AI-generated images.

Takeaways: From AI Monoculture to Civilizational Permaculture

To understand the stakes, we can look to a powerful lesson from agriculture: the perils of monoculture.

Monoculture farming, while offering the illusion of efficiency and higher yields, ultimately degrades the soil, destroys biodiversity, and leaves the entire system vulnerable to collapse. It trades long-term resilience for short-term productivity.

An “AI Monoculture” poses a similar civilisational risk. If our AI systems are trained on and optimized for a narrow band of dominant cultural data, they risk degrading our collective “cultural soil,” eroding the diversity of human thought that is our greatest strength.

The path forward, then, is not to abandon the technology but to use it to cultivate a Civilisational Permaculture. This approach uses AI to foster a resilient, interconnected ecosystem where thousands of cultures can thrive and cross-pollinate. This philosophy should guide our actions.

For Individuals & Creators

  • Use diverse cultural prompts when interacting with AI (try asking for Indian wedding traditions, then Nigerian, then Japanese).
  • Seek out AI tools specifically designed for cultural diversity like Aya by Cohere (supports 101 languages) or SeamlessM4T by Meta for multilingual communication.
  • Contribute to cultural preservation projects like Wikitongues or local digitization efforts.

For Technologists & Developers

  • Implement cultural diversity metrics in AI evaluation (following examples like Google's Monk Skin Tone Scale methodology).
  • Use techniques like few-shot learning to better represent low-resource cultures with limited training data.
  • Partner with cultural institutions for authentic, representative datasets.

For Cultural Institutions

  • Create high-quality digital archives that can serve as "seed stock" for AI training.
  • Collaborate with AI companies to ensure cultural content is properly represented and compensated.
  • Develop digital sovereignty frameworks that protect indigenous knowledge while enabling beneficial AI applications.

Conclusion

Our biological DNA provides shared human hardware, but our diverse civilizational DNA represents the varied cultural software that has driven human innovation and resilience.

For us to go from Being to Thrive as humans in the Age of AI, we face a civilizational question:
Will we allow this powerful technology to flatten our rich cultural landscape into a homogeneous monoculture, or will we consciously cultivate a thriving digital permaculture?

The answer lies not in the technology itself, but in how we choose to deploy it. Every dataset we create, every algorithm we train, every cultural preservation project we fund is a vote for the kind of digital future we want to inhabit.

What should we do — allow AI to lead us to a monoculture and civilizational risk, or a permaculture that helps humans grow? What do you think?

As always, I welcome your comments, insights, and ideas.


Explore, engage, share, and stay tuned for more.

Best wishes.

References:

“AI is a language. Treat it like one: practice, iterate, and mind your grammar prompts, assumptions, and verification.”

II. Questioning / Asking

Good conversations flow from well‑sequenced questions—topical, simple, coherent, cohesive.
LLM Conversation Example 1
Q: What are empirical judgments?

A: Empirical judgments are based on observation, experience, or experimentation.
Q: What are moral judgments?

A: Moral judgments are based on ethical principles and values.

IV. Judgment

“The structure of a language affects its speakers’ worldview and cognition.”
— Henry Hazlitt
“The art of questioning is the source of all knowledge.”
— Thomas Berger
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