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Introduction

Introduction
Complete

Data Integrity: The Pillar of Trustworthy AI​

Imagine you’re preparing a scientific experiment. You’ve got the right hypothesis and the right tools—but if your samples are contaminated or unevenly distributed, your results won’t be trustworthy.

Data handling in machine learning works the same way. Before we can train or optimize a model, we must ensure that the data it learns from is accurate, representative, and reliable. Every split, sample, and cleaning step affects how well your model will perform once it’s deployed.

Hand pointing illustration

Why does this unit matter?​

Even the most advanced machine learning algorithm can fail if the data behind it isn’t handled correctly. Hidden data leakage, class imbalance, or poor data quality can quietly distort your model’s performance and lead to unreliable or biased results.

In this unit, you’ll learn how to prepare your data strategically—splitting it the right way, detecting and preventing leakage, and addressing imbalance to create fair and dependable models.

Learning objectives​

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

  • Design robust data splitting and cross-validation strategies to ensure reliable model evaluation and prevent data leakage.
  • Implement techniques for handling imbalanced datasets to improve model performance and fairness.
  • Develop protocols to handle data quality issues to improve overall model reliability.
Data split illustration

Action item: Pause and think​

Before diving in, take a moment to think about the following questions.

Questions & reflections
1. Have you ever seen a model perform well during training but fail on new data? What might have caused that?

Type your reflection here...

2. What would change in your workflow if you approached data handling as a critical design step rather than a preparation task?

Type your reflection here...