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Introduction

Introduction
Complete

Have you ever hesitated before using a dataset?​

Have you ever hesitated before using a dataset, unsure whether it was even legal or ethical to use it?

As ML becomes embedded in products and services across sectors, public concern around data use is rising. Organisations are now expected to explain how their systems use personal data, justify retention periods, and show how bias and privacy risks are being addressed. Frameworks like AREA and SAFE-D provide structure to this effort—but they must be applied thoughtfully.

In this unit, you’ll unpack how privacy and governance considerations shape the design, quality, and trustworthiness of ML systems from start to finish.

Finger pointing illustration

Why does this unit matter?​

In today’s data-driven world, it’s not enough to build powerful machine learning models, you also need to ensure they are built on trustworthy, legally compliant data.

Whether you’re working with customer transactions, patient records, or demographic datasets, the way you collect, store, govern, and use that data has serious implications for individual privacy, regulatory compliance, and your organisation’s reputation. These skills aren’t just essential for compliance—they’re central to building trustworthy AI.

Learning objectives​

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

  • Analyse comprehensive data and information security standards, ethical practices, and policies relevant to ML data management activities.
  • Critically assess the implications of various data governance frameworks on ML systems, considering regulatory requirements, data privacy, security, trustworthiness, and quality control.
  • Design ML data management strategies that ensure compliance with relevant regulations while maintaining data lineage, retention, and metadata management best practice.
  • Design and implement a comprehensive data quality control framework tailored for ML pipelines, addressing issues of data bias, completeness, and consistency.

Action item: Pause and think​

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

Reflection: Data Governance & Privacy
1. Have you ever worked with or built an ML model using sensitive or personal data?

Type your reflection here...

2. What governance or privacy concerns did you encounter—or overlook—and how did that affect the project?

Type your reflection here...