Algorithms and Bias

Overview

This 60-minute lesson introduces Secondary 4 students to the concept of algorithms, explores how bias can manifest in algorithms and artificial intelligence, and discusses the ethical implications of these biases. Activities will build critical media literacy skills essential for navigating today’s digital world, fostering responsible citizenship aligned with Quebec’s Education Program.

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Quebec Curriculum Links

Cycle: Secondary IV
Broad Area of Learning (AÉC): Citizenship and Community Life
Cross-Curricular Competency (CC):

• CC1: Exercise critical judgement
• CC2: Use information
• CC3: Act ethically and responsibly
• CC4: Cooperate with others

Subject Competency (Social Studies):

• Demonstrate critical thinking to analyse social phenomena and issues
• Communicate using a variety of forms adapted to purpose and audience

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Learning Objectives

By end of lesson, students will be able to:

1. Define “algorithm” and explain how algorithms function in everyday technology.
2. Identify at least two examples of bias within commonly used algorithms and AI systems (e.g. facial recognition, recruitment tools).
3. Analyse the potential social and ethical consequences of algorithmic bias on various communities.
4. Collaborate to propose ethical guidelines or future considerations for developing equitable technologies.

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Materials Needed

• Whiteboard or interactive board
• Displays with vocabulary list
• Printed brief case studies (facial recognition, recruitment AI)
• Graphic organizer handouts
• Sticky notes or small cards for group project
• Timer

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Lesson Flow

1. Introduction & Vocabulary Building (10 minutes)

Goal: Establish foundational understanding of key terms related to algorithms and bias.

• Write these terms on board: Algorithm, Artificial Intelligence (AI), Bias, Discrimination, Data Set, Ethical Implications.
• Briefly define each term using simple, relatable language (e.g., algorithm = “a set of instructions for computers to solve problems or make decisions”).
• Conduct a quick “Think-Pair-Share”: Students discuss with a partner examples of algorithms they encounter daily (search engines, social media feeds, video game AI).
• Share out 2-3 examples as a class.

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2. Case Study Exploration – Identifying Bias (20 minutes)

Goal: Deepen understanding via real-world examples.

• Divide class into 5 groups of five.

• Assign each group one case study printed summary:
  a) Facial recognition software and racial bias
  b) AI recruitment tools disadvantaging women/minorities
  c) Predictive policing algorithms and over-policing certain communities
  d) Social media feed algorithms and echo chambers
  e) Loan approval algorithms and socioeconomic bias

• Students read and underline key points describing how bias entered the algorithm and its consequences.

• Groups complete a graphic organizer noting:

  • What is the algorithm’s purpose?
  • Where/how does bias occur?
  • Who is affected and how?
  • Ethical questions or concerns raised

• Each group shares a 2-minute summary with the class.

Discussion prompts:

• How can these biases harm individuals or groups?
• Do algorithms reflect or amplify existing societal prejudices?

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3. Group Project: Ethics for Future Technologies (25 minutes)

Goal: Encourage metacognition and ethical reasoning about technology development.

• Maintain the same groups.

• Challenge groups to brainstorm and draft a brief “Ethical Charter” for developers creating algorithms/AI.

• Prompt cards with guiding questions (placed on desk):

  • How can developers reduce bias?
  • What responsibilities do companies have toward society?
  • How should algorithms be tested before release?
  • What transparency should users expect?
  • How can users contest unfair decisions made by AI?

• Each group lists 3-5 concrete ethical guidelines or principles on sticky notes or cards.

• Groups post their guidelines on a designated wall/board and briefly explain their choices.

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4. Conclusion & Assessment (5 minutes)

Goal: Reflect and assess understanding.

• Whole class discussion:
  • Why is it important for citizens to understand algorithms and biases?
  • How might your own actions impact the use or development of these technologies?

Exit Ticket (formative assessment):
On a small piece of paper, students write:

• One new thing they learned about algorithms or bias
• One ethical question or concern they have about technology in society

Collect for review.

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Teacher Notes & Differentiation

• Encourage bilingual vocabulary reinforcement for English and French learners by pairing students to translate or explain terms in their preferred language.
• Use multimedia (short videos or images) beforehand to illustrate case studies if technology permits.
• For quieter students, allow written reflection time before group discussion.
• Emphasize critical thinking and ethical reasoning over technical jargon.
• Frame discussions to respect diverse perspectives and lived experiences of all students.

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This lesson aims to empower students as informed, critical digital citizens aware of technology’s power and limitations within our modern society, a key mandate of Quebec’s education framework.