Module 1 - Lesson 4: Bias in, bias out

Learners will explore how bias can appear in machine learning models due to the data used to train them. They will create their own machine learning model to classify images of apples and tomatoes, and discover how a limited dataset can lead to biased and inaccurate predictions. Finally, they will investigate two types of bias that can appear in training data.

Learning objectives

  • Describe the impact of data on the accuracy of a machine learning (ML) model
  • Explain the need for both training and test data
  • Explain how bias can influence the predictions generated by an ML model

Key vocabulary

Training data, Test data, Accuracy, Bias, Data bias, Societal bias

Lesson structure

  • Data types for classification
  • Supermarket AI application
  • Bias
  • Optional: Your future career

Download lesson resources

To download the lesson plan, slides and activities please log in with or create a free Raspberry Pi Foundation account.
We will then ask you a few questions to help us understand how you will use the resources.