The Rise of Relational Technology: How Indigenous Wisdom is Rewriting the Rules of AI
Over 70% of AI projects fail to make it to production, often due to a fundamental disconnect: a lack of trust and reciprocal benefit for those whose data fuels these systems. But a growing movement of artists, technologists, and Indigenous thinkers are challenging this extractive model, building AI not for people, but with them – and drawing on millennia of Indigenous knowledge to do so. This isn’t about integrating Indigenous cultures into existing tech; it’s about building something fundamentally new.
Beyond Consent: The Flaws of Western Data Models
The current paradigm of data collection operates on a foundation of assumed consent, buried within lengthy and often incomprehensible terms of service. Suzanne Kite, a Lakota artist and technologist, argues this is a critical flaw. Her work, including the installation WičhíŋCala Šakówiŋ (Seven Little Girls), demonstrates a radical alternative. This piece translates Kite’s dance movements into audio responses via sensors embedded in a four-meter braid, anchored by Lakota star maps. It’s a system where data isn’t passively extracted, but actively generated through a reciprocal relationship between artist and machine, body and algorithm. “It’s my data. It’s my training set,” Kite explains, emphasizing the intimate control and understanding inherent in her approach. This contrasts sharply with the “black box” nature of large language models, where the origins and biases of training data are often opaque.
More-Than-Human Intelligence and Reciprocal Systems
Kite’s work embodies what she calls “more-than-human intelligence” – a concept rooted in the Lakota principle of reciprocity. This isn’t simply about ethical data handling; it’s about recognizing intelligence as emerging from relationships, not existing as an isolated entity. Her installation, Ínyan Iyé (Telling Rock), further explores this, featuring an AI-powered rock that “speaks” and responds to viewers, challenging conventional notions of agency. The rock’s responses aren’t pre-programmed; they evolve through interaction, mirroring a conversational exchange. This approach directly confronts the extractive logic of many AI systems, where data is taken without offering anything in return.
Reclaiming Indigenous Technologies: A History of Innovation
The narrative that Indigenous cultures are separate from technology is a false one. As these artists demonstrate, Indigenous peoples have always been innovators, developing sophisticated systems of knowledge and practice deeply intertwined with the land and its resources. Nicholas Galanin’s work powerfully illustrates this point. His installation, Some of the poorwroneousness are not come down from the Levi, features a mechanical drum that relentlessly beats at the tempo of a heartbeat, a stark reminder of the cultural memory embedded within traditional Tlingit drums. His sculpture, I think it goes like this (pick yourself up), indicts the historical sabotage of Native technologies, contrasting the enduring nature of carved wood data storage with the fragility of digital records.
Sound, Performance, and the Spectral Presence of the Past
The challenge to traditional AI isn’t limited to visual art. Raven Chacon’s Pulitzer Prize-winning composition, Voiceless Mass, utilizes sound to create a “technological séance,” generating frequencies that exploit the acoustics of a cathedral to evoke historical absences. Crucially, each performance is recorded only with explicit consent, mirroring Kite’s emphasis on reciprocal data exchange. This approach highlights the potential of sound and performance to confront the logic of surveillance and extraction, creating spaces for remembrance and resistance.
The Future of AI: From Extraction to Relationship
These artists aren’t simply critiquing existing AI systems; they’re building prototypes for a different future. A future where intelligence is not gathered through passive extraction, but cultivated through active relationship. This shift requires a fundamental rethinking of how we define data, consent, and agency. It demands a move away from centralized, opaque models towards decentralized, transparent systems that prioritize reciprocity and mutual benefit. The work of Kite, Chacon, and Galanin suggests that the key to unlocking a more ethical and sustainable AI lies not in technological advancement alone, but in rediscovering and honoring the wisdom of Indigenous knowledge systems. Cultural Intellectual Property Rights are becoming increasingly important in this context.
What will it take for this relational approach to become the norm? The answer likely lies in a broader cultural shift – a recognition that technology is not neutral, and that its development must be guided by principles of justice, equity, and respect for all living beings. Share your thoughts on the future of relational technology in the comments below!
