Welcome to the workshop!
Workshop
In ProgressWelcome to Governance and Compliance Simulation - Building a Regulatory-Ready ML System
Today's icebreaker:
Scramble for compliance
Rearrange the following letters to uncover a key ML compliance risk—and explain how regulatory compliance or governance in ML systems can help mitigate it.
I – B – S – A
Share your response in the chat!
Today's agenda:
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Review:
Recap key concepts.
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Practice:
Building a compliance plan.
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Closing:
Key takeaways and next steps.
Today's learning objectives:
- Analyse how governance frameworks (e.g., AREA, SAFE-D) shape compliant ML data strategies and operations.
- Apply regulatory requirements (e.g., GDPR, AI Act) to design compliant ML system practices, including data minimisation, access control, and transparency measures.
- Create compliance artefacts (e.g., risk matrices, audit checklists) that demonstrate readiness for external audits and regulatory reviews.
- Design escalation protocols for responding to compliance breaches in ML systems.
Workshop slides
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In progress