SPRAPP Governance Frameworks: A Practical Guide

Why Governance Matters for Council of AIs

As organizations deploy multi-model AI systems for critical decisions, governance becomes non-negotiable. An ungoverned LLM council is a liability waiting to happen.

Core Governance Principles

1. Transparency

Every council decision should be explainable:

• Log which models participated
• Record individual model outputs
• Document consensus methodology
• Store confidence scores

2. Accountability

Define clear ownership:

• Who configured the council?
• Who approved the model selection?
• Who reviews disputed consensus?
• Who handles escalation?

3. Auditability

Enable retrospective analysis:

• Complete decision history
• Model performance tracking
• Bias detection over time
• Outcome correlation analysis

Governance Framework Components

Model Selection Policy

Document criteria for including models in your SPRAPP:

- Accuracy requirements per domain
- Latency constraints
- Cost thresholds
- Data residency requirements
- Compliance certifications

Consensus Protocols

Define how your council of LLMs reaches decisions:

• Unanimous: All models must agree
• Majority: Simple voting mechanism
• Weighted: Expert models have more influence
• Hierarchical: Senior models can overrule

Human Oversight Triggers

Specify when humans must review council outputs:

• Confidence below threshold (e.g., 70%)
• High-stakes domains (medical, legal, financial)
• Novel situations outside training distribution
• Disagreement above tolerance level

Implementation Checklist

• Document council configuration
• Establish review cadence
• Create escalation procedures
• Define metrics and KPIs
• Train oversight personnel
• Set up audit logging
• Plan regular audits