Symbiotic Alignment via Collective Predictive Coding: A Theoretical Framework for Co-Creative Human-AI Ecosystems

Area of Science:

• Artificial Intelligence
• Societal Impact of AI
• Computational Social Science

Background:

• AI algorithms optimizing for individual engagement exacerbate societal polarization.
• Current AI alignment paradigms (e.g., RLHF) are insufficient for collective dynamics.
• Emergent risks necessitate new approaches to human-AI interaction.

Purpose of the Study:

• Propose symbiotic alignment (SA) as a novel framework for human-AI interaction.
• Shift from unilateral control to mutual adaptation and co-evolution.
• Address systemic risks of AI, including deep polarization.

Main Methods:

• Grounding SA in collective predictive coding (CPC) and symbol emergence systems (SES).
• Formalizing human-AI interaction using multi-agent reinforcement learning (MARL).
• Introducing a collective regularization term to minimize collective free energy (CFE) while preserving autonomy.

Main Results:

• Demonstrating that social coherence does not necessitate uniformity.
• Computationally reinterpreting "plurality" as a stable multimodal distribution of shared beliefs.
• Showing diverse worldviews can coexist through mutual negotiation.

Conclusions:

• Symbiotic alignment offers a path beyond current AI alignment limitations.
• Future research should focus on AI agents capable of co-creative learning.
• Developing social mechanisms ("gardeners") to foster trust and steer technological futures toward plurality.