Computer Science · Grade 10

Active learning ideas

Bias in AI and Algorithms

Active learning works well for this topic because students need to see bias in action, not just hear about it. Handling real data and flawed algorithms lets them experience firsthand how human choices shape technology. Discussions and revisions make abstract concepts concrete and memorable.

• Ontario Curriculum Expectations
• CS.HS.S.8
• CS.HS.S.9

• 30–45 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Socratic Seminar
• 45 min · Small Groups

Case Study Stations: Real-World Bias

Prepare stations with printouts on cases like COMPAS recidivism prediction or Google's image labeling errors. Small groups spend 10 minutes per station identifying bias sources, impacts, and one fix, then rotate and compile class findings on a shared chart.

Analyze how implicit biases can be embedded in AI training data.

Facilitation Tip

During Case Study Stations, circulate to ensure groups stay focused on one bias source at a time, not drifting into general opinions.

What to look for

Present students with a short scenario describing an AI system (e.g., a university admissions predictor). Ask them to identify one potential source of bias in the data or algorithm and explain how it might lead to an unfair outcome.

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

Activity 02

• Socratic Seminar
• 30 min · Pairs

Dataset Dissection: Hunt for Imbalance

Provide sample datasets such as facial images or job applicant profiles skewed by gender or ethnicity. Pairs tally representations, graph disparities, and discuss how these skew outcomes, presenting one key insight to the class.

Critique real-world examples of algorithmic bias and their societal impact.

Facilitation Tip

When students Dissect Datasets, have them document every imbalance they find with clear counts and percentages to ground their arguments.

What to look for

Facilitate a class discussion using the prompt: 'Imagine you are developing a new AI tool to recommend job candidates. What steps would you take during data collection and model development to actively prevent bias?' Encourage students to share specific strategies.

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

Activity 03

• Socratic Seminar
• 40 min · Small Groups

Mitigation Role-Play: Fix the Algorithm

Assign roles like data scientist, ethicist, and stakeholder to small groups facing a biased hiring AI scenario. They brainstorm and prototype three mitigation steps, such as fairness audits, then pitch solutions in a 2-minute class showcase.

Propose strategies to mitigate bias in the development and deployment of AI systems.

Facilitation Tip

In the Mitigation Role-Play, assign specific roles (data scientist, ethicist, user) to push students beyond vague fixes and into detailed trade-offs.

What to look for

Provide students with a case study of algorithmic bias (e.g., biased sentencing algorithms). Ask them to write down one societal consequence of this bias and one proposed mitigation strategy discussed in class.

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

Activity 04

• Socratic Seminar
• 35 min · Whole Class

Bias Debate: Deploy or Delay?

Divide the class into teams debating whether to deploy a biased loan algorithm with partial fixes. Each side prepares arguments from prior activities, debates for 20 minutes, and votes with justifications.

Analyze how implicit biases can be embedded in AI training data.

Facilitation Tip

For the Bias Debate, require each side to include at least one mitigation strategy in their opening statements to keep arguments solution-focused.

What to look for

Present students with a short scenario describing an AI system (e.g., a university admissions predictor). Ask them to identify one potential source of bias in the data or algorithm and explain how it might lead to an unfair outcome.

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

A few notes on teaching this unit

Teachers should model how to examine bias step-by-step, breaking down complex systems into data, algorithm, and outcome. Avoid rushing through examples; let students sit with discomfort when unfair outcomes appear. Research shows that structured peer discussion and revision cycles help students move from noticing bias to addressing it effectively.

Successful learning looks like students identifying bias in multiple contexts, explaining how it enters AI systems, and proposing fair solutions. They should justify their reasoning with evidence from datasets, design choices, and societal impacts. Collaboration and reflection deepen their understanding beyond individual understanding.

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Watch Out for These Misconceptions

• During Dataset Dissection, watch for groups assuming that larger datasets automatically correct bias without checking their composition.

  Have students calculate representation ratios in their datasets and ask them to explain why raw counts alone do not eliminate bias. Provide a side-by-side comparison of balanced and imbalanced datasets to highlight the difference.

• During Mitigation Role-Play, watch for students treating bias as a simple coding error instead of a design trade-off.

  Require each group to document one fairness metric they will prioritize and explain how improving it might affect another metric. Use mock code snippets to show that fixes often create new challenges.

• During Bias Debate, watch for students claiming that bias is unavoidable and thus acceptable.

  Prompt teams to propose at least one concrete step they would take to reduce bias in the system they are discussing, using evidence from earlier activities to support their claims.

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Methods used in this brief

• Socratic Seminar