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.

Verification standardsAPI specificationsSecurity requirementsPerformance benchmarks

Economic Policies

Community decisions about resource allocation, pricing mechanisms, and incentive structures.

Revenue sharingFee structuresToken distributionGrant programs

Ethical Guidelines

Development of principles and policies for responsible AI development and deployment.

Bias preventionFairness standardsSafety requirementsPrivacy protection

Access and Inclusion

Ensuring the network serves diverse communities and promotes equitable access to AI capabilities.

Accessibility standardsGlobal participationCommunity supportEducational programs

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

Network Decisions

Community governance of network infrastructure, policies, and technical standards that affect all participants and services.

Model Governance

Stakeholder governance for individual AI models deployed on Grid and served through Cortex, including access policies and ethical guidelines.

Verified Decisions

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.