Lesson 3: Bias in, bias out

Create a machine learning model to classify images and explore how a limited data set can lead to bias.

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

Artificial intelligence (AI), machine learning (ML), supervised learning, classification, training data, test data, accuracy, bias, data bias, societal bias

Lesson structure

  • The three different types of machine learning
  • Supermarket AI application
  • Training a model
  • Bias
  • Student timetable model
  • Reducing bias

Lesson overview

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