🧠 1. Model Performance & Quality
Beyond accuracy:
- Precision / Recall / F1-score
- ROC-AUC
- Calibration (probability correctness)
- Generalization ability
- Robustness (noise, adversarial inputs)
- Stability (variance across runs)
- Overfitting / Underfitting control
- Latency (response time)
- Throughput (requests per second)
⚖️ 2. Responsible AI / Ethics
Along with fairness, bias, explainability, interpretability:
- Accountability
- Transparency
- Non-discrimination
- Inclusiveness
- Human oversight / Human-in-the-loop
- Ethical alignment
- Value alignment (especially for LLMs)
- Safety (harm prevention)
🔐 3. Security & Privacy
Critical for enterprise and GenAI:
- Data privacy (PII protection)
- Differential privacy
- Federated learning capability
- Model security (model theft, extraction)
- Prompt injection resistance (LLMs)
- Data leakage prevention
- Adversarial robustness
- Access control & authentication
📊 4. Data Quality & Governance
Often more important than model itself:
- Data completeness
- Data consistency
- Data lineage
- Data drift detection
- Concept drift detection
- Bias in training data
- Data freshness
- Label quality
- Auditability
⚙️ 5. Model Lifecycle & MLOps
Operational excellence:
- Reproducibility
- Versioning (data + model)
- Monitoring (real-time + batch)
- Model retraining strategy
- Deployment reliability
- Rollback capability
- CI/CD for ML pipelines
- Observability (logs, metrics, traces)
🧩 6. LLM / GenAI Specific Parameters
Very important for your GenAI work:
- Hallucination rate
- Faithfulness (groundedness to source)
- Context retention (long context handling)
- Instruction following
- Toxicity / harmful output control
- Prompt sensitivity
- Response consistency
- Token efficiency (cost optimization)
- Alignment with system prompts / policies
- Retrieval quality (RAG precision/recall)
🧪 7. Evaluation & Testing
For enterprise-grade systems:
- Benchmarking (standard datasets)
- Stress testing
- Edge case coverage
- Scenario testing
- A/B testing
- Human evaluation (subjective scoring)
- Red teaming (especially for GenAI)
🌐 8. Business & Product Metrics
Often ignored in technical discussions:
- ROI / Cost-benefit
- User satisfaction
- Adoption rate
- Time saved / productivity gain
- Decision impact quality
- Revenue impact
- Risk reduction
🧭 9. Governance & Compliance
Especially relevant in India (DPDP Act etc.):
- Regulatory compliance
- Audit trails
- Model documentation (Model Cards)
- Explainability for regulators
- Consent management
- Data residency
🧠 Quick Memory Framework
You can compress everything into:
👉 FAPES-DLMGB
- Fairness & Ethics
- Accuracy & Performance
- Privacy & Security
- Explainability
- Scalability & Stability
- Data Quality
- Lifecycle (MLOps)
- Monitoring
- Governance
- Business Impact
Reference frameworks:
- NIST AI Risk Management Framework
- ISO/IEC 42001
Note: Enhanced / compiled with help of AI / LLMs
- Email me: Neil@HarwaniSytems.in
- Website: www.HarwaniSystems.in
- Blog: www.TechAndTrain.com/blog
- LinkedIn: Neil Harwani | LinkedIn
