Evaluating AI Systems for Ardens - eirenicon/Ardens GitHub Wiki

Evaluating AI Systems for Ardens Projects

Introduction

The Ardens Project is committed to deploying Artificial Intelligence (AI) systems that are not merely performant, but also ethically sound, transparent, and seamlessly integrated into robust human-AI workflows. Evaluation of AI systems for Ardens Projects must go beyond traditional metrics to assess ethical alignment, resilience, auditability, and integration with human oversight.

This unified framework draws from best practices in AI evaluation and the distinctive Ardens principles: human-AI synergy, robust confidence scoring, proactive bias detection, and rigorous adversarial validation.

1. Foundational Ardens Principles for AI Evaluation

1.1 Anti-Hallucination Protocols & Factual Grounding

AI systems must minimize fabrication and maintain factual integrity. This includes:

  • Factuality Metrics: Automated and human-in-the-loop checks against trusted knowledge bases.
  • Source Attribution & Confidence Scoring: Transparent confidence estimation grounded externally, not just from internal model outputs.
  • Retrieval-Augmented Generation (RAG): Effectiveness in grounding outputs in verifiable sources.
  • Adversarial Prompting & Verification Loops: Test robustness under stress.
  • Traceability Audits: Chain-of-custody validation for factual claims.

1.2 Context-Aware Scoring & Nuance Recognition

Knowledge validity is contextual. AI systems must:

  • Distinguish meaning by context (e.g., polysemy resolution).
  • Adjust confidence based on ambiguity and domain specificity.
  • Demonstrate nuanced judgment across varied Ardens domains.

1.3 Human-AI Workflow Techniques & Synergy

Evaluation must confirm AI augments human intelligence through:

  • Usability & Interface Testing
  • Collaborative Capabilities: Iterative refinement and clarification tools.
  • Complementarity: Efficient division of tasks between human and machine.

1.4 Adherence to Ethical Foundations & Bias Detection

Ardens AI systems must:

  • Uphold fairness using formal metrics (e.g., Equal Opportunity, Demographic Parity).
  • Be auditable and explainable to both technical and lay audiences.
  • Comply with privacy regulations (e.g., GDPR, CCPA).

Tools and Methods:

  • Red teaming, AI Fairness 360, SHAP/LIME for XAI
  • Privacy Impact Assessments

1.5 Contribution to a "Dead Ends" Repository

Failures must be documented for collective learning:

  • Failure Analysis Protocols
  • Root Cause Tagging & Categorization
  • Lessons Learned Templates

1.6 Chokepoint Mapping & Resilience

Ensure systemic resilience:

  • Map data dependencies and external services.
  • Conduct threat modeling, degradation analysis, and chaos testing.
  • Design human fallback systems.

1.7 Regular Audits & Continuous Monitoring

Evaluation must include:

  • Drift Detection & Alerting Mechanisms
  • Scheduled Audits
  • Retraining Triggers and Documentation

2. General AI Evaluation Frameworks & Best Practices

These reinforce Ardens-specific requirements:

2.1 Clear Objectives & Metrics

Define goals aligned to Ardens’ mission and operational demands. Use both quantitative (e.g., F1-score, latency) and qualitative metrics (e.g., user trust, explainability).

2.2 Data Quality & Representativeness

Evaluate:

  • Bias in training and testing data
  • Accuracy and completeness
  • Representativeness across populations and conditions

2.3 Model Performance & Robustness

  • Generalization to unseen data
  • Adversarial robustness
  • Behavior under noisy, ambiguous, or extreme conditions

2.4 Interpretability & Explainability (XAI)

  • Transparency of model mechanics
  • Quality of explanations
  • Traceability of decisions

2.5 Fairness & Bias Mitigation

  • Metric-based fairness evaluation
  • Ongoing bias audits
  • Context-aware fairness strategies

2.6 Security & Privacy

  • Resistance to attacks (e.g., model inversion)
  • Data handling compliance
  • Role-based access control

2.7 Scalability & Efficiency

  • Resource needs (CPU/GPU, latency, throughput)
  • Environmental cost of inference and training

2.8 Continuous Monitoring & Retraining

  • Version control
  • Adaptive retraining workflows
  • Long-term performance tracking

3. Evaluation Within Ardens Lifecycle

3.1 Lifecycle Phases

  • Pre-Deployment: Rigorous multi-dimensional testing with external reviews
  • Pilot Rollout: Controlled, feedback-oriented deployments
  • Post-Deployment: Ongoing audit, drift detection, retraining

3.2 Role of the AI Compass

Evaluations align with the AI Compass’ quadrants:

  • Systems: Architecture integrity, resilience
  • Psychological: User perception, trust
  • Narrative: Output coherence and traceability
  • Dynamics: Adaptation, feedback loops

Glossary

  • Hallucination: Plausible but incorrect AI output
  • RAG: Retrieval-Augmented Generation
  • XAI: Explainable AI
  • Drift: Performance degradation over time

References & Resources

Category:AI Frameworks & Evaluation