Model Selection & Documentation
What This Requires
Document model selection rationale for each AI system: evaluation criteria, alternatives considered, benchmark results, and trade-offs (accuracy vs. cost, latency vs. complexity). Publish Model Cards for custom models.
Why It Matters
Opaque model selection leads to suboptimal choices and makes debugging difficult. Documentation enables auditability, knowledge transfer, and informed decisions during model refresh cycles.
How To Implement
Define Evaluation Criteria
For each use case, specify requirements: accuracy threshold, max latency, cost per query, explainability needs, compliance constraints. Weight criteria by importance.
Benchmark Alternatives
Test 2-3 candidate models on representative data. Measure accuracy, latency, cost, and bias metrics. Document results in comparison matrix.
Selection Rationale
Write 1-2 page summary explaining chosen model, why alternatives were rejected, and known trade-offs (e.g., "GPT-4 chosen over Claude for better structured output, accepting higher cost").
Publish Model Card
For custom models, create Model Card (template from Google, Hugging Face) documenting: intended use, training data, performance metrics, limitations, bias analysis, ethical considerations. Store with asset inventory.
Evidence & Audit
- Model selection criteria for each AI system
- Benchmark comparison matrix showing alternatives evaluated
- Selection rationale documents
- Model Cards for custom models
- Approval records showing decision-maker sign-off