Operationalising Responsible AI
summary
Maps fairness, transparency and robustness to concrete nonfunctional requirements and implements them as codified fitness functions that run in CI/CD (examples: test coverage ≥90%, maintainability ≤5%). Validates data with checks for completeness (age and gender present), correctness (age 15–100), and validity (blood sugar ≤3 months), enforces data versioning and immutable transformers, decomposes models (categorization → feature extractor), continuously monitors bias via true positive rate equality across demographics, enforces model serve <500 ms and structured, PII-free logs for observability.
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chapters (10)
- 0:00Introduction to Responsible AI
- 8:25Understanding Fair AI Dimensions
- 15:25Mapping Fair AI to Non-Functional Requirements
- 22:25Data Quality as the Foundation
- 32:41Data Pipeline Modularity
- 37:22Algorithmic Decomposability
- 41:19Monitoring Beyond Algorithmic Performance
- 49:54Incremental Change and AB Testing
- 55:12Observability in AI Systems
- 57:22Tools and Next Steps
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