Hong Kong AI Delivery Practices
Tags: Frameworks
TL;DR
- Detailed practices mapped to each lifecycle stage form the playbook for ethical AI delivery.
- Assessment questions reference these practices to guide implementation.
Why it matters for HK marketers: It turns principles into concrete tasks for martech teams and vendors.
Practice areas by lifecycle
- Project Strategy: Organisation strategy; internal policies/practices; industry standards and regulations.
- Project Planning: Portfolio management; project oversight and delivery approach.
- Project Ecosystem: Technology roadmap for AI/data; procuring AI services with ethical considerations; change management.
- Project Development: Business & data understanding; solution design and appropriate human intervention; data extraction with quality/validity checks; pre-processing with privacy/security; model building to mitigate errors (e.g., overfitting) and adversarial risks.
- System Deployment: Model integration & impact verified via V&V/testing; transition & execution with failure mitigation steps; ongoing monitoring; evaluation & check-in ensuring traceability, repeatability, reproducibility.
- System Operation and Monitoring: Continuous data/model performance monitoring; operational support; continuous review/compliance for new/revised laws and regulations.
How it connects to assessment
- Many impact assessment questions point directly to these practice areas, ensuring consistency between planning and review.
6 lifecycle stages each have defined practice areas for execution.
So what for marketers
Use the guide to structure SOWs with vendors and to define acceptance criteria and monitoring SLAs for AI in campaigns and platforms.
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