AI Risk Management Framework (AI RMF 1.0)

1. Overview & Purpose

The AI Risk Management Framework (AI RMF 1.0) establishes standardized protocols for evaluating and managing risks throughout the lifecycle of AI models integrated into Aevum Encyclopedia. As our platform increasingly leverages machine learning for content verification, semantic search, and multilingual translation, this framework ensures that all AI-driven processes remain transparent, accountable, and aligned with ethical research standards.

This document applies to all internal AI deployments, third-party model integrations, and contributor-facing AI tools. It aligns with international standards including NIST AI RMF, ISO/IEC 42001, and the EU AI Act.

2. Core Principles

All AI systems operating within Aevum Encyclopedia must adhere to the following foundational principles:

• Transparency: Clear documentation of model capabilities, limitations, and data provenance.
• Accountability: Defined ownership for AI outcomes with traceable decision pathways.
• Equity & Fairness: Active mitigation of bias across languages, regions, and subject domains.
• Safety & Reliability: Robust testing for hallucination rates, adversarial vulnerability, and operational stability.
• Human Oversight: Final editorial authority always rests with verified human contributors.

3. Framework Pillars

The AI RMF 1.0 operates on four interdependent pillars that cover the complete AI lifecycle:

3.1 Govern

Establish organizational culture, risk tolerance thresholds, and oversight structures. Includes policy documentation, stakeholder mapping, and AI ethics committee charters.

3.2 Map

Identify and catalog AI system risks across data ingestion, model training, inference, and deployment stages. Utilizes dynamic risk registers and dependency graphs.

3.3 Measure

Quantify risks using standardized metrics: hallucination frequency, bias disparity indices, toxicity scores, and compliance drift rates. Automated scoring runs on every model update.

3.4 Manage

Implement mitigation strategies, continuous monitoring, and incident response protocols. Includes rollback procedures, human-in-the-loop interventions, and public disclosure workflows.

4. Risk Taxonomy & Classification Matrix

Risks are categorized by domain and severity. The following matrix guides prioritization and response escalation:

5. Implementation Guide

Teams deploying AI features must follow the standardized integration pipeline:

1. Pre-Deployment Assessment: Submit model card, training dataset manifest, and risk matrix to the Governance Portal.
2. Sandbox Validation: Run automated evaluation suite covering accuracy, safety, and compliance benchmarks.
3. Staged Rollout: Begin with internal contributors, expand to verified editors, then public-facing integration.
4. Continuous Monitoring: Enable real-time telemetry logging and anomaly detection dashboards.
5. Documentation & Disclosure: Update public model registry with version, capabilities, and known limitations.

// Example: Risk Evaluation Hook
const riskAssessment = await aiRMF.evaluate({
  modelId: 'aevum-llm-v3.2',
  dataset: 'encyclopedia_corpus_2025',
  thresholds: {
    hallucination: 0.05,
    bias_index: 0.15,
    toxicity: 0.02
  }
});

if (!riskAssessment.compliant) {
  aiRMF.flag('deployment_halt', riskAssessment.metrics);
}

6. Compliance & Auditing

All AI systems must undergo biannual third-party audits. Audit reports are published in redacted form to maintain transparency while protecting proprietary model architecture. The Compliance Dashboard provides real-time status tracking for all deployed models.

7. Version History

Future updates will be announced via the Aevum Governance Newsletter and developer mailing list.