Bias and fairness
Bias and fairness: systems trained on historical data reproduce historical unfairness unless someone actively intervenes.
AI for Kids · Class 9 · Module 8 of 9
Ethics, Careers and Society
The hardest AI questions have no code in them at all.
8 sessionsSessions 57–64
4 weeksTypical pace
AppliedLevel
Class 9Artificial Intelligence
The big idea
Students examine bias and fairness with real cases, look honestly at how AI is reshaping work, and learn the basics of how governance and regulation attempt to keep up.
Why it matters
Students will spend their careers alongside these systems. Informed opinions beat inherited ones.
Unpacked
What you will actually learn
Three core ideas, each taught with worked examples and then practised until it feels obvious.
Work and careers
Work and careers change unevenly — tasks are automated before whole jobs, and new roles appear that nobody had named five years earlier.
Governance basics
Governance basics cover transparency, accountability and the emerging regulation across different countries.
Did you know?
A well-documented recruitment tool had to be scrapped after it learned to downgrade CVs containing the word “women’s”, because it was trained on a decade of biased hiring.
Common mix-up
“Algorithms are neutral.” Every algorithm encodes choices about what to optimise and what to ignore. Neutrality is not available.
Session by session
Your 8-session journey
Sessions 57–64 of the 72-session year, at two one-hour sessions per week.
- 1
Warm-up and big picture
Where this module fits, what you will build, and a hands-on starter that gets everyone curious about bias and fairness.
- 2
Bias and fairness
Guided teaching on bias and fairness, worked through together with the teacher.
- 3
Bias and fairness — practice lab
Independent practice, small challenges and one deliberate mistake to diagnose.
- 4
Work and careers
Guided teaching on work and careers, building directly on the previous two sessions.
- 5
Work and careers — practice lab
Applied tasks that combine bias and fairness and work and careers in one piece of work.
- 6
Governance basics
Governance basics introduced and practised, completing the toolkit needed for the project.
- 7
Project build
Guided build session for the module project: Student AI policy debate.
- 8
Test, present and reflect
Finish, test against the checklist, present the work and explain the decisions behind it.
Project lab
Student AI policy debate
Every module ends with something the student built themselves and can demonstrate. This is the piece that goes into their portfolio and gets explained out loud at the end of session 64.
Try this at home
Debate a real AI policy question from the side you disagree with. It is the fastest way to find the weak points in your own view.
It is finished when
- It works from start to finish without breaking
- You can explain every part of it in your own words
- You tested it and improved at least one thing afterwards
- Someone else used or understood it without your help
By the end of this module
Students finishing Module 8 can:
- Explain and use bias and fairness without prompting
- Explain and use work and careers without prompting
- Explain and use governance basics without prompting
- Build and finish student ai policy debate
- Test your own work and correct what you find
- Talk an adult through what you made and why
Word bank
The vocabulary introduced here, in plain language:
- algorithmic bias
- Systematic unfairness in a system’s outputs.
- accountability
- Being answerable for a system’s effects.
- governance
- The rules and oversight applied to technology.
Module challenge
Think you have got this?
0 XPLevel 1 · Curious Beginner
6 quick questions drawn from this module — vocabulary, the project you build, and a myth-or-fact round. Every wrong answer explains itself, so a mistake still teaches you something.
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