How do I know what my AI has learned?

For RAG systems: Check what documents are in the knowledge base. For fine-tuned models: Keep records of training data. Test the model with scenarios. Use logging: Track what AI outputs for each input. You cannot directly 'see' into a model's weights, but you can audit training data, test outputs, and check what information is retrievable.

Can I see why AI made a decision?

Partial. LLMs are 'black boxes'—their internal reasoning isn't directly visible. But: Ask AI to explain its reasoning, test with variations to see sensitivity, use tools that trace prompt→output, review which training sources are retrieved. Not perfect transparency, but can investigate major decisions.

Why audit AI systems?

Audit for: bias detection (is AI treating groups unfairly?), accuracy verification (is AI correct?), compliance (regulations require documentation), incident investigation (when AI fails), continuous improvement (fix what's wrong), and stakeholder trust (show you're responsible). Regular audits catch problems before they cause damage.

How Do I Audit What My AI System Has Learned?

AI isn't a black box you can't see into. Here's how to audit what your AI knows and how it decides.

What to Audit

Audit Area	What to Check
Training Data	What data was it trained on?
Knowledge Base	What documents can it access?
Outputs	What responses does it produce?
Behavior	How does it handle edge cases?
Bias	Does it treat groups differently?

Why Auditing Matters

Bias detection: Catch discrimination early
Accuracy: Verify AI is correct
Compliance: Regulations require documentation
Incident response: Investigate failures
Trust: Stakeholders want transparency

Auditing RAG Systems

For retrieval-based AI:

Document inventory: What's in the knowledge base?
Retrieve test: Query and see what documents AI pulls
Citation check: Are cited documents accurate?
Gap analysis: What's missing that should be there?

Auditing Fine-Tuned Models

For trained AI:

Training data records: Keep detailed logs
Test scenarios: Standardized test cases
Output metrics: Track performance over time
Comparison: How has behavior changed from base model?

Output Testing

Test what AI produces:

Standard test set: Known inputs with expected outputs
Edge cases: Unusual inputs
Adversarial: Try to break it
Real-world: Actual user queries

Bias Testing

Check for discrimination:

A/B variations: Same query, different demographics
Outcome comparison: Are responses different?
Language analysis: Different tone for groups?
Historical check: Does AI learn from biased data?

Logging Everything

Keep comprehensive records:

All inputs: What users asked
All outputs: What AI responded
Sources used: Which documents retrieved
Timestamps: When interactions occurred
User ID: Who interacted (anonymized for privacy)

Explainability Techniques

Ways to understand decisions:

Ask AI: "Explain your reasoning"
Source citations: Require citations for claims
Step-by-step: Show chain of thought
Sensitivity testing: Change input slightly, see impact

Regular Audit Schedule

Audit Type	Frequency
Output quality check	Weekly
Bias testing	Monthly
Full system audit	Quarterly
Training data review	When updated
Incident investigation	As needed

Compliance Requirements

Japan and international:

EU AI Act: Documentation required for high-risk AI
Japan guidelines: AI transparency recommendations
Industry-specific: Financial, healthcare have own rules
Internal policy: Company AI governance

When AI Fails

Post-incident audit:

Document failure: What went wrong?
Trace cause: Why did AI produce that output?
Check data: Was training data the issue?
Fix: Update system, training, or rules
Prevent: Add tests for this scenario

Greene Solutions Approach

Auditing built in:

Comprehensive logging on all systems
Regular audit reports for clients
Bias testing included in implementation
Incident response procedures

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