Algorithmic Bias Deep Dive

Overview

This 60-minute class session is designed for Secondary 4 students (Grade 10) within the Quebec Education Program (QEP) framework, continuing the unit on Algorithms and Bias. This lesson will deepen students’ understanding of the causes of AI bias, including developer diversity and biased training data, through real-world examples (e.g., police facial recognition, recruitment algorithms). It emphasizes critical thinking, debate, and research skills aligned with the QEP’s Cross-Curricular Competencies and Social Sciences competencies.

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Context in the Quebec Curriculum

Relevant Competencies and Learning Objectives

• General Orientations – Cross-curricular Competencies:

  • Competency C2: Uses information
  • Competency C3: Exercise critical judgment
  • Competency C4: Adopts effective work methods
  • Competency C5: Communicates effectively

• Secondary Cycle Two (Sec 4) Social Sciences (Citizenship and Ethics):

  • Describe and analyze societal issues related to technology
  • Evaluate causes and effects of social inequalities, including those derived from digital tools and algorithms
  • Explore ethical dilemmas linked to technological practices
  • Construct arguments and hypotheses supported by evidence

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Learning Objectives

By the end of the lesson, students will be able to:

1. Explain how factors such as developer diversity and biased training data contribute to AI bias.
2. Analyze real-world cases (police facial recognition, recruitment algorithms) illustrating algorithmic bias.
3. Critically evaluate differing viewpoints on solutions to algorithmic bias during structured debates.
4. Demonstrate effective research skills by compiling credible evidence for their group projects on technology’s future impact.
5. Collaborate respectfully and communicate their ideas clearly in group discussions and debates.

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Materials Needed

• Projector or interactive whiteboard
• Printed or digital case study excerpts about facial recognition and recruitment algorithms
• Graphic organiser handouts for note-taking and debate preparation
• Internet access for brief directed research (library or tablet carts)
• Whiteboard markers or chart paper for debate outcomes
• Timer for debate sessions

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Lesson Breakdown (60 minutes)

Time	Activity	Description	Quebec Curriculum Links
0–10 min	Introduction + Recap	Brief review of previous lesson on AI bias concept and general causes. Introduce today’s focus: deeper causes (developer diversity and biased data). Use a quick visual aid/video snippet (2-3 min) introducing cases.	C3: Critical judgment; Social studies 4th cycle: Analysis of societal tech issues
10–25 min	Case Study Exploration	Students work in pairs to analyse two real-world AI bias cases: 1) Police facial recognition algorithm bias against minorities, 2) Recruitment algorithms disadvantaging women/minorities. Provide excerpts and guided questions. Students identify causal factors and societal impacts.	C2: Uses information; Social studies: Identify societal impacts and technology-related inequalities
25–35 min	Class Debate Setup	Explain the debate format: two teams per case will argue about proposed solutions to algorithmic bias (e.g., increasing developer diversity, auditing training data, or regulatory frameworks). Distribute graphic organisers outlining pros and cons. Allow brief planning time.	C3: Critical judgment; C5: Communication; Social studies: Construct arguments
35–50 min	Debates	Conduct two short debates (approx. 7-8 min each). Encourage respectful listening and evidence-based argumentation. Teacher moderates, emphasises critical and ethical dimensions. Each debate ends with a collective synthesis on the whiteboard.	C3, C5; Social studies: Ethical dilemmas and argumentation
50–60 min	Research Skills Mini-Workshop + Project Prep	Guide students on finding credible sources, evaluating bias, and citing information. Connect this with their ongoing group projects on technology’s future. Students individually list 2 research questions relevant to their projects and possible sources.	C2, C4: Research and effective working methods

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Assessment Strategies

• Formative:

  • Observation of pair work analysing case studies
  • Participation and quality of arguments during debates
  • Completeness and clarity of graphic organiser and research questions

• Summative (Preparation):

  • Students’ research questions and source lists will be used as part of their group project work checklist aligned with QEP expectations for project work at Cycle 2

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Differentiation and Adaptations

• Provide bilingual (English/French) handouts as needed for mixed language classrooms.
• Encourage students with diverse learning needs to use voice recordings or mind maps for their debate prep.
• For more advanced learners, suggest exploring intersectionality of different bias causes or ethical frameworks guiding AI.

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Extension Ideas for Future Lessons

• Simulation activity creating biased data sets and testing algorithm outcomes
• Inviting a guest speaker working in AI ethics or local civil rights organization
• Using coding platforms to experiment with simple algorithms and observing bias

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This lesson plan contextualizes algorithmic bias within students’ lived realities and promotes higher-order thinking through critical analysis and debate—all within the framework of the Quebec curriculum’s competencies and ethical citizenship goals.