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Introduction

Introduction
Complete

Machine learning isn’t just about models.​

It’s about the entire system. From training data and algorithms to deployment pipelines and user interfaces, each component introduces potential risks.

As ML systems integrate into critical services, they face a growing web of laws (like the EU AI Act) and standards (like ISO) aimed at protecting users and ensuring fairness. This unit will teach you how to protect the entire ML system by understanding compliance, identifying risks, and designing audit-ready processes.

Finger pointing illustration

Why does this unit matter?​

Building ML models comes with serious risks—from algorithmic bias to compliance violations. The stakes are high in sectors like healthcare and finance, where failures lead to reputational damage or real-world harm.

While Unit 2 focused on data, Unit 3 broadens your perspective to the entire ML system, ensuring your models and processes are audit-ready and aligned with evolving standards.

Learning objectives​

By the end of this unit, you will be able to:

  • Analyse the landscape of legislation, regulation, and governance frameworks applicable to ML and AI.
  • Develop risk assessment and mitigation strategies addressing both digital and physical supply chain vulnerabilities.
  • Design and implement comprehensive audit processes that ensure compliance with industry regulations and standards.

Action item: Pause and think​

Before diving in, take a moment to reflect on the following questions:

Reflection: Compliance & Risk
1. Have you ever worked on or heard of an ML project that faced legal, ethical, or operational risks? What went wrong?

Type your reflection here...

2. How do you think a strong compliance and risk management strategy could have changed the outcome?

Type your reflection here...