Hong Kong AI Lifecycle Practice Guide
Tags: Frameworks
Purpose
The AI Practice Guide operationalises the framework’s principles by defining practice areas mapped to the AI Lifecycle. These practices are referenced directly by the AI Application Impact Assessment (AAIA).
Practice Areas by Lifecycle
- Organisation Strategy, Internal Policies & Practices – Embed explainability and ethical decision‑making standards.
- Industry Standards & Regulations – Assess adherence to applicable laws and standards.
- Portfolio Management – Ensure AI projects align with business requirements and objectives.
- Project Oversight & Delivery Approach – Quality control across project deliverables per the Project Management Plan.
- Technology Roadmap for AI & Data – Plan what, when, and which technologies to procure.
- Procuring AI Services – Address ethical considerations in third‑party product/data procurement.
- Business & Data Understanding – Define objectives; balance benefits and risks of AI in decision‑making.
- Solution Design – Assess model suitability and required level of human intervention.
- Data Extraction – Assure data quality, validity, reliability, and consistency from internal/external sources.
- Pre‑processing – Protect sensitive data; prevent leakage and privacy/security breaches.
- Model Building – Mitigate common errors (e.g., incorrect assumptions, overfitting, adversarial attacks).
- Model Integration & Impact – Verification, validation, and testing against defined requirements.
- Transition & Execution – Implement failure mitigation steps prior to deployment.
- Ongoing Monitoring – Maintain model performance and robustness via feedback.
- Evaluation & Check‑in – Ensure traceability, repeatability, reproducibility; build public/stakeholder trust.
- System Operation and Monitoring
- Data & Model Performance Monitoring – Continuous review to counter model drift and relevance loss.
- Operational Support – Maintain consistent, reliable, robust application performance.
- Continuous Review/Compliance – Monitor for non‑compliance with new/revised laws and regulations (roles: Project Manager/Maintenance Team, PSC/PAT or Maintenance Board, IT Board/CIO or delegates).
Integration with Assessment
These practices are directly referenced in AAIA questions, ensuring concrete controls support the Ethical AI Principles at each lifecycle stage.
Sources:- Ethical_AI_Framework_en.pdf
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