Fairness in AI Decisions

Active learning helps students grasp fairness in AI by turning abstract concepts into concrete experiences. When students role-play, analyze datasets, and debate scenarios, they directly see how biases emerge in technology that often appears neutral.

• Year 6
• Technologies
• 4 activities
• 30 min–45 min

Learning Objectives

1. Explain why an AI might make a decision that appears unfair based on its training data.
2. Compare and contrast the decision-making processes of humans and AI in specific scenarios.
3. Identify potential biases in AI decision-making within everyday digital tools.
4. Propose simple modifications to AI systems to promote fairer outcomes.

Case Study Analysis

⏱ 30 min·Pairs

Role-Play: Human vs AI Judge

Divide class into pairs: one acts as a human judge, the other as an AI using predefined rules on cards. Present scenarios like hiring or game penalties; switch roles and discuss differences. Groups report one key insight on fairness.

Prepare & details

Explain why an AI might sometimes make a decision that seems unfair.

Facilitation Tip: For the Role-Play activity, assign clear roles and provide scripted scenarios so students focus on comparing human empathy with AI pattern-matching rather than improvising.

Setup: Groups at tables with case materials

Materials: Case study packet (3-5 pages), Analysis framework worksheet, Presentation template

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Case Study Analysis

⏱ 45 min·Small Groups

Bias Hunt: Dataset Analysis

Provide printed datasets on faces or names with imbalances. In small groups, students tally representations, predict AI outputs, then test with a simple sorting app. Discuss how to fix imbalances.

Prepare & details

Compare how a human makes a decision versus how an AI might make one.

Setup: Groups at tables with case materials

Materials: Case study packet (3-5 pages), Analysis framework worksheet, Presentation template

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Case Study Analysis

⏱ 40 min·Small Groups

Scenario Debate Carousel

Post 4-5 AI fairness scenarios around the room. Groups visit each for 5 minutes, note pros/cons of AI decisions, then rotate to build on prior notes. Whole class votes on fairest solutions.

Prepare & details

Discuss a simple scenario where an AI's decision could affect people differently.

Setup: Groups at tables with case materials

Materials: Case study packet (3-5 pages), Analysis framework worksheet, Presentation template

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Case Study Analysis

⏱ 35 min·Individual

Fair AI Design Challenge

Individuals sketch an AI tool for school use, list decision rules, and flag potential biases. Pairs review and refine, then share with class for feedback.

Prepare & details

Explain why an AI might sometimes make a decision that seems unfair.

Setup: Groups at tables with case materials

Materials: Case study packet (3-5 pages), Analysis framework worksheet, Presentation template

• Analyze
• Evaluate
• Create
• Decision-Making
• Self-Management

Teaching This Topic

Teachers should emphasize comparison—between human and AI decision-making, and between biased and unbiased data. Avoid letting discussions stay theoretical. Use structured activities to anchor abstract ideas in student experiences, as research shows this builds deeper understanding of fairness concepts in young learners.

What to Expect

Successful learning looks like students identifying specific biases in datasets, explaining how AI decisions differ from human judgment in relatable terms, and designing fairer AI systems. Clear evidence of this includes reasoned arguments during debates and thoughtful design choices in their challenges.

Watch Out for These Misconceptions

Common Misconception

During the Role-Play: Human vs AI Judge activity, watch for students who assume the AI judge will always be correct because it uses data.

What to Teach Instead

Use the role-play to highlight how AI relies on flawed data. After the activity, have students compare their AI judge's decision with the human judge's reasoning to show how context and empathy fill gaps that data alone cannot.

Common Misconception

During the Bias Hunt: Dataset Analysis activity, watch for students who think biases are intentional rather than accidental flaws in data.

What to Teach Instead

Guide students to focus on patterns in the data that disadvantage groups without anyone meaning to create those patterns. Ask them to identify which groups are affected and why the data might have missed them.

Common Misconception

During the Fair AI Design Challenge activity, watch for students who believe fairness means treating everyone exactly the same way.

What to Teach Instead

Use the design challenge to push students toward equitable solutions. Have them explain why identical treatment might not work for different groups, and ask them to redesign their AI to account for varying needs.

Assessment Ideas

Discussion Prompt

After the Scenario Debate Carousel, present students with the reading program scenario. Ask them to explain why the AI's decision might be unfair and how a teacher might decide differently. Listen for references to data bias and human context in their responses.

Quick Check

During the Bias Hunt: Dataset Analysis activity, ask students to write down one bias they found in the dataset and one way it could affect an AI decision in school, such as grouping students for activities.

Exit Ticket

After the Fair AI Design Challenge, ask students to define 'bias' in their own words and give one example of how it could affect an AI decision in a school setting, like choosing teams for a game.

Extensions & Scaffolding

• Challenge: Ask early finishers to design a dataset that would produce fairer outcomes for a given scenario, then justify their choices in writing.
• Scaffolding: Provide sentence starters for students who struggle to articulate differences between human and AI decisions, such as 'The AI would _____ because it looks at _____, while a human would _____ because they consider _____.'
• Deeper: Invite students to research real-world examples of biased AI (e.g., facial recognition errors) and present their findings to the class.

Key Vocabulary

Algorithm	A set of step-by-step instructions that a computer follows to solve a problem or complete a task, like making a decision.
Bias	A tendency to favor one thing, person, or group over another, which can lead to unfair outcomes in AI decisions.
Training Data	The information and examples used to teach an AI system how to make decisions or predictions.
Fairness	The quality of treating people equally and without prejudice, which is a goal for AI decision-making.