Computing · Year 10

Active learning ideas

Algorithmic Bias and Fairness

Active learning helps students grasp algorithmic bias because fairness is not just a technical detail but an ethical and societal issue. Debates and case studies make abstract concepts visible by connecting them to real-world harm, which builds critical thinking that lectures alone cannot achieve.

• National Curriculum Attainment Targets
• GCSE: Computing - Environmental and Ethical Impacts

• 35–50 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Socratic Seminar
• 40 min · Pairs

Debate Pairs: Algorithm Neutrality

Pair students to prepare arguments for and against algorithms being truly neutral, using evidence sheets on data sources. Pairs debate for 4 minutes each, then switch sides. End with whole-class vote and reflection journal.

Can an algorithm ever be truly neutral if it is trained on data created by humans?

Facilitation Tip

During Debate Pairs, assign roles clearly and provide sentence stems to guide structured arguments about algorithm neutrality.

What to look for

Present students with a scenario: 'An AI is developed to recommend job candidates. It is trained on data from a company that historically hired more men for technical roles.' Ask: 'What potential biases might this AI develop? How could these biases impact job seekers?'

• Analyze
• Evaluate
• Create
• Social Awareness
• Relationship Skills

Activity 02

• Stations Rotation
• 45 min · Small Groups

Stations Rotation: Bias Case Studies

Set up stations for facial recognition, hiring tools, and predictive policing with articles and data visuals. Small groups spend 10 minutes per station noting bias sources and impacts, then share findings. Rotate twice for full coverage.

Analyze how algorithmic bias can perpetuate or amplify societal inequalities.

Facilitation Tip

For Station Rotation, prepare each case study with a short reading, a bias audit checklist, and a reflection prompt to keep groups on task.

What to look for

Provide students with a short description of an AI system (e.g., a content moderation tool). Ask them to identify one potential source of bias in its design or data and one negative consequence it might have.

• Remember
• Understand
• Apply
• Analyze
• Self-Management
• Relationship Skills

Activity 03

• Socratic Seminar
• 35 min · Pairs

Dataset Audit: Pairs Analysis

Provide sample datasets in spreadsheets showing imbalances like gender in job titles. Pairs calculate disparities, hypothesize causes, and suggest fixes like reweighting. Present one fix to class.

Critique methods for identifying and mitigating bias in artificial intelligence systems.

Facilitation Tip

In Dataset Audit, give pairs a sample dataset with known skews and ask them to calculate representation gaps before proposing fixes.

What to look for

Students write down one fairness metric they learned about and briefly explain in their own words how it helps identify bias. They should also list one challenge in applying fairness metrics in practice.

• Analyze
• Evaluate
• Create
• Social Awareness
• Relationship Skills

Activity 04

• Socratic Seminar
• 50 min · Small Groups

Fairness Protocol Workshop

Small groups design a 5-step audit protocol for an imaginary loan algorithm, testing it on mock data. Groups peer-review protocols, then vote on the strongest. Compile class best practices.

Can an algorithm ever be truly neutral if it is trained on data created by humans?

Facilitation Tip

During the Fairness Protocol Workshop, provide a template fairness checklist so students focus on actionable steps rather than abstract ideas.

What to look for

Present students with a scenario: 'An AI is developed to recommend job candidates. It is trained on data from a company that historically hired more men for technical roles.' Ask: 'What potential biases might this AI develop? How could these biases impact job seekers?'

• Analyze
• Evaluate
• Create
• Social Awareness
• Relationship Skills

A few notes on teaching this unit

Teachers approach this topic by grounding discussions in concrete examples students can critique, not abstract theory. Use structured turn-and-talk routines to surface misconceptions before formalizing corrections. Avoid rushing to solutions, as the goal is for students to see bias as a design flaw, not a bug to fix quickly. Research shows that role-play and perspective-taking deepen ethical reasoning more than lectures.

Students will explain how training data and design choices create bias, identify fairness metrics, and propose solutions that reduce harm. They will articulate why objectivity is not automatic in computing and how diverse perspectives improve system design.

---

Watch Out for These Misconceptions

• During Debate Pairs, students may claim algorithms are objective because they lack emotions. Watch for this during the neutrality debate when pairs argue pros and cons.

  Redirect the pair to examine the word-association activity from Station Rotation. Have them revisit the dataset examples and trace how gendered stereotypes appear in training data, not in the algorithm itself.

• During Station Rotation, students might believe adding more data automatically reduces bias. Watch for this when pairs discuss data volume versus diversity.

  Ask students to input a skewed dataset into the audit tool and add more of the same skewed data. Guide them to observe how representation gaps grow, then prompt them to try balancing the data and compare outcomes.

• During Dataset Audit, students may focus only on harm to minority groups. Watch for this when pairs list societal impacts.

  Provide the collaborative impact chart template and ask each pair to add one scenario where majority groups face bias. Use the chart to show how bias affects all users depending on context.

---

Methods used in this brief

• Socratic Seminar
• Stations Rotation