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Year 10 - Computing

Bias and accuracy in machine learning

Using data science and AI tools effectively and safely

Unit Summary

In this unit, pupils will explore how data science and AI tools are having an impact on our daily lives. They will develop an understanding of how to use these tools effectively as well as an awareness of the issues relating to trust, bias and misinformation.

Lesson Summary

You will learn to describe the impact of data on ML models and explain bias in ML model predictions.

Key Notes

  • The type, quality and amount of data used significantly affect how accurate a machine learning (ML) model is.
  • ML models require both training data and separate test data to ensure reliability.
  • Bias is introduced into ML models when the data is unrepresentative or contains stereotypes.
  • Use large, representative data sets and diverse perspectives to reduce bias and improve ML model fairness and accuracy.

Vocabulary To Learn

  • fair: when something is free from bias and gives equal consideration and treatment to all parts of a group or situation
  • unrepresentative: when the data does not properly reflect the whole group or situation it is meant to describe
  • diverse: when data includes a wide range of examples that fairly represent different parts of the whole group or situation

Common Mistakes To Avoid

  • ML models are neutral and always give fair or accurate information.

3 Quick Questions (With Answers)

1. Describe the system or process from this lesson in clear steps.

The type, quality and amount of data used significantly affect how accurate a machine learning (ML) model is. ML models require both training data and separate test data to ensure reliability.

2. Define this computing term and give one practical example. 'fair'

when something is free from bias and gives equal consideration and treatment to all parts of a group or situation. Add one real device or system example to prove understanding.

3. Correct this common computing misconception.

Mistake: ML models are neutral and always give fair or accurate information. Correction: ML models can reflect or amplify bias in their training data. If the data contains stereotypes or imbalances, the ML model can repeat or reinforce them.

More Lessons In This Unit

Browse all guides in the Year 10 Computing guide library.