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Async review

Instruction and application
In Progress

Recap core topics:​

  • Unit 2: Data Privacy and Governance in Machine Learning
  • Unit 3: Regulatory Compliance and Risk Management in Machine Learning

Unit 2: Data Privacy and Governance in Machine Learning​

In Unit 2, you explored…

  • Privacy and ethical foundations– Key regulations such as GDPR, HIPAA, and CPRA, and the importance of privacy-by-design when handling sensitive ML data.
  • Governance frameworks – AREA and SAFE-D as tools to embed accountability, fairness, and explainability across ML systems.
  • Compliant data strategies – Practices for data minimisation, metadata tracking, retention limits, and access control to meet legal and ethical standards.
  • Quality and fairness – Frameworks to assess data completeness, consistency, and bias, and ensure equitable model performance across user groups.
  • Trust through transparency – How strong governance, clear documentation, and inclusive review practices build confidence in ML system outcomes.

Unit 3: Regulatory Compliance and Risk Management in Machine Learning​

In Unit 3, you explored…

  • ML compliance frameworks – Key regulations and standards, including the EU AI Act, ISO/IEC 23053, and NIST AI RMF, and how they guide safe, transparent ML development.
  • System-level risk – Common vulnerabilities across the ML lifecycle, from third-party dependencies and infrastructure gaps to model failure and versioning issues.
  • Mitigation and escalation – Proactive practices like workflow gating, anomaly thresholds, and peer reviews, alongside tiered escalation protocols to manage incidents.
  • Audit readiness – Documentation and processes needed to demonstrate compliance, including model cards, access logs, and risk registers.
  • Organisational accountability – Responsibility mapping across roles, ensuring risks are owned and addressed across development, deployment, and monitoring stages.

What’s at risk in ML systems?​

Common compliance risks in real-world ML projects:

  • Privacy breaches from poor data governance.
  • Fairness gaps due to unrepresentative or biased data.
  • Lack of transparency in how models make decisions.
  • Missing controls for access, auditability, or incident response.

Governance and compliance foundations in ML​

Core frameworks shaping ML practices:

  • Regulations: GDPR, AI Act, HIPAA, CCPA – define boundaries for privacy, fairness, and data use.
  • Standards: ISO/IEC 23053, NIST AI RMF – guide system interoperability, risk control, and continuous monitoring.
  • Frameworks: AREA and SAFE-D – support accountability, explainability, fairness, and ethical oversight across the ML lifecycle.Compliance artefacts****Key artefacts likerisk matrices, audit checklists, and escalation plans help teams anticipate and manage compliance risks.

Action item: Quick reflection - Governance in your role​

Which governance or compliance concept feels most relevant to your role—and why?

Think about:

  • Where your current work intersects with regulation, transparency, or risk.
  • How applying these principles could strengthen trust in your ML systems. Share your response in the chat!