Algorithmic Bias Impact

Context

This is Lesson 2 of 4 in the unit Exploring Algorithms and Bias. It is designed for Grade 10 Social Studies students in Quebec. The lesson aligns with the Quebec Secondary Cycle Two Social Studies program, addressing critical and ethical thinking, as well as understanding societal issues related to digital technology.

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Learning Objectives

By the end of this 75-minute lesson, students will be able to:

• Understand and explain how algorithmic bias manifests in different societal sectors (law enforcement, healthcare, hiring).
• Analyse real-world examples of algorithmic bias and its consequences, particularly in relation to marginalized communities.
• Critically evaluate the ethical considerations and social impacts of biased algorithms.
• Propose informed solutions or strategies to detect and mitigate algorithmic bias.

Quebec Curriculum References

Competencies targeted (from Social Studies and cross-curricular):

• CE2.01: Explain significant social developments and their impacts.
• CE2.02: Analyse and interpret information and data to understand societal phenomena.
• CC1.01: Apply critical thinking to evaluate information sources and arguments.
• CF1.01: Demonstrate ethical judgment in various contexts.

Cross-disciplinary Links:

• Information and Communication Technologies (ICT) competency from Québec Education Program:
  • Use digital tools to solve problems and communicate responsibly (ICT2).

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Materials Required

• Projector and screen for multimedia presentation
• Printed or digital case study handouts (3-4 case studies across sectors)
• Whiteboards or large chart paper + markers
• Sticky notes for brainstorming
• Student notebooks or devices for note-taking

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Lesson Breakdown (75 minutes)

1. Introduction and Context Setting (10 minutes)

• Begin with a brief multimedia presentation defining algorithms and algorithmic bias, including examples in everyday life (e.g., social media filters, search engine results).
• Discuss the role algorithms play in decision-making across societal sectors.
• Pose a thought-provoking question: What could happen if these algorithms are biased?
• Link discussion to Quebec’s values of equity and social justice to frame significance.

2. Real-world Examples Exploration (15 minutes)

• Divide class into 4 small groups (5 students each).
• Distribute different case studies illustrating algorithmic bias in:
  1. Law enforcement (predictive policing skewed by racial data)
  2. Healthcare (bias in diagnostic algorithms affecting minority patients)
  3. Hiring practices (AI screening disadvantaging candidates)
  4. Education or credit scoring systems (optional sector)
• Each group reviews their case study and fills out a guided worksheet covering:
  • What type of bias is present?
  • Who is affected?
  • What are the consequences?

3. Group Sharing & Discussion (15 minutes)

• Each group briefly presents their case to the class (3 minutes each).
• Facilitate a whole-class discussion highlighting the disproportionate impact on marginalized communities, linking to Quebec’s social values and principles of democracy (Curriculum reference to “Citizenship and social participation”).
• Use whiteboard/chart paper to map common themes of bias and effects noted across sectors.

4. Interactive Problem-Solving Activity (20 minutes)

• Present the question: How can we reduce algorithmic bias to make society fairer?
• Students work in their groups to brainstorm and document practical solutions (e.g., increased transparency, diverse data sets, human oversight). Use sticky notes for ideas.
• Encourage referencing Quebec's Charter of Human Rights and Freedoms as a framework for ethical solutions.
• Groups share back their top two solutions. Teacher synthesizes ideas and emphasizes importance of ethical responsibility in technological development.

5. Reflection and Ethical Considerations (10 minutes)

• Students individually write a paragraph responding to the prompt:
  “What is the most important ethical issue around algorithmic bias, and how should society address it?”
• Optionally, invite some students to share reflections aloud.
• Teacher links reflections to Quebec’s emphasis on critical thinking and ethical citizenship.

6. Closing and Assessment (5 minutes)

• Quick formative assessment:
  • Students submit their group worksheets and reflection.
  • Exit ticket on sticky notes: one new insight and one question about algorithmic bias they still have.
• Teacher previews next lesson: designing unbiased algorithms and ethical AI.

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Differentiation and Inclusion

• Provide bilingual key vocabulary handout (English/French) on algorithmic bias for ELL and French immersion students.
• Offer case studies with varying levels of complexity for diverse learners.
• Encourage collaborative roles within groups (reader, recorder, presenter) to engage all students.
• Use visual aids and real-life examples accessible to varied learning styles.

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Teacher’s Tips to Impress

• Use local Québec or Canadian examples where possible (e.g., CRA use of algorithms, provincial healthcare diagnostics).
• Incorporate short, engaging videos or animated explainers (teacher’s choice, no URLs included here).
• Encourage students to question data sources and the power dynamics behind algorithm development — connect to broader societal themes studied in Social Studies.
• Highlight how students themselves as digital citizens interact with algorithms daily and emphasize empowerment through knowledge.

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This comprehensive plan ensures students critically understand algorithmic bias’s societal impacts and are better prepared to engage ethically with technology, fully aligned with Quebec's Social Studies curriculum goals.