Democratic AI Governance
Community-Driven Decisions
Tenzro Governance enables democratic decision-making for AI systems, from network-wide policies to individual model governance, ensuring AI serves community interests rather than corporate profits.
Why AI Needs Democratic Governance
Current Governance Problems
AI systems that affect millions of people are controlled by a few companies who make decisions based on profit rather than community needs.
Users, affected communities, and even researchers have little input into how AI systems are developed, deployed, or modified over time.
Important decisions about AI ethics, safety, and social impact are made behind closed doors without transparency or accountability.
Democratic Alternative
Stakeholder governance ensures that everyone affected by AI systems has a voice in decisions about how those systems operate and evolve.
Transparent, verifiable decision-making processes create accountability and enable community oversight of AI development and deployment.
Diverse perspectives in governance lead to more robust, fair, and beneficial AI systems that serve community needs rather than corporate interests.
Layers of Governance
Different types of decisions require different governance approaches, from network-wide infrastructure to specific AI models and projects.
Network Governance
Community decisions about the foundational infrastructure, policies, and technical standards that affect the entire ecosystem.
Model Governance
Stakeholder governance for individual AI models, including training decisions, access policies, and ethical guidelines.
Project Governance
Collaborative decision-making for research projects, datasets, and community initiatives within the network.
Governance Capabilities
Tools and frameworks that enable effective, inclusive, and transparent decision-making for AI systems at any scale.
Stakeholder Representation
All affected parties have a voice in decisions, from developers and users to communities impacted by AI systems.
Transparent Processes
All proposals, discussions, and voting records are publicly visible with cryptographic verification.
Flexible Mechanisms
Different voting methods and decision-making processes can be adapted to specific contexts and requirements.
Democratic Participation
Community members can propose changes, participate in discussions, and vote on important decisions.
Expert Input Integration
Technical experts and domain specialists can provide input on complex decisions while maintaining democratic oversight.
Compliance Coordination
Governance frameworks help coordinate compliance with different regulatory requirements across jurisdictions.
Areas of Governance
Community governance covers technical, economic, ethical, and social aspects of AI development and deployment.
Technical Standards
Decisions about protocols, security standards, and technical specifications that ensure network interoperability.
Economic Policies
Community decisions about resource allocation, pricing mechanisms, and incentive structures.
Ethical Guidelines
Development of principles and policies for responsible AI development and deployment.
Access and Inclusion
Ensuring the network serves diverse communities and promotes equitable access to AI capabilities.
Who Participates in Governance
Effective AI governance requires diverse perspectives from all stakeholders who are affected by or contribute to AI systems.
Model Developers
People and organizations creating AI models who have expertise in technical implementation.
Role in Governance:
Technical input on model capabilities, training requirements, and implementation feasibility.
End Users
Individuals and organizations who use AI services and are affected by governance decisions.
Role in Governance:
User experience feedback, accessibility requirements, and service quality standards.
Affected Communities
Groups and communities impacted by AI systems, even if they don't directly use them.
Role in Governance:
Social impact assessment, ethical review, and fairness evaluation.
Domain Experts
Specialists in relevant fields like ethics, law, security, and specific application domains.
Role in Governance:
Expert analysis, risk assessment, and technical guidance on complex decisions.
Resource Contributors
People providing computational resources, data, or other infrastructure to the network.
Role in Governance:
Infrastructure policy input, resource allocation decisions, and operational standards.
Governance Principles
These principles guide how governance systems are designed and operated to ensure they serve community interests effectively and fairly.
Inclusive Participation
All stakeholders should have meaningful opportunities to participate in decisions that affect them.
Transparency and Accountability
Decision-making processes should be open, documented, and subject to community oversight.
Proportional Representation
Voting power should reflect stake and expertise while preventing concentration of control.
Adaptive Governance
Governance structures should evolve with the network and respond to changing needs and circumstances.
Governance Across the Ecosystem
Community governance of network infrastructure, policies, and technical standards that affect all participants and services.
Stakeholder governance for individual AI models deployed on Grid and served through Cortex, including access policies and ethical guidelines.
All governance decisions are recorded in the Ledger with cryptographic verification, ensuring transparency and preventing manipulation.
Shape the Future of AI
Join a community that believes AI should be governed by the people it affects, not just the companies that build it.