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Year 10 - Computing

Algorithmic bias

Algorithms

Unit Summary

In this unit pupils will discover the computational thinking techniques; decomposition, abstraction and algorithmic thinking. They will reflect on differences between algorithms and computer programs and explore how algorithms can be represented. Pupils will explore the impact of algorithmic bias.

Lesson Summary

You will learn to describe algorithmic bias and suggest ways to make algorithms fairer.

Key Notes

  • Algorithmic bias is when an algorithm produces unfair or discriminatory outcomes that favour some groups over others.
  • Using poor quality data in algorithms can lead to inaccurate results, unreliable predictions, or biased outcomes.
  • Algorithms can become biased if the design of the algorithm instructions are biased.
  • By aiming to understand sources of bias algorithmic, bias can be reduced as much as possible.

Vocabulary To Learn

  • bias: to disproportionately favour one side, group, or outcome over others
  • discriminatory: to make or show an unjust or prejudicial distinction between different categories of people

Common Mistakes To Avoid

  • Algorithmic bias can be completely eliminated.

3 Quick Questions (With Answers)

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

Algorithmic bias is when an algorithm produces unfair or discriminatory outcomes that favour some groups over others. Using poor quality data in algorithms can lead to inaccurate results, unreliable predictions, or biased outcomes.

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

to disproportionately favour one side, group, or outcome over others. Add one real device or system example to prove understanding.

3. Correct this common computing misconception.

Mistake: Algorithmic bias can be completely eliminated. Correction: It is extremely difficult, maybe impossible, for complex algorithms to be completely free from bias. By aiming to understand sources of bias, actively working to reduce it, and being transparent, algorithmic bias can be reduced as much as possible.

More Lessons In This Unit

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