Algorithms don’t just follow rules — they reflect the data and decisions used to create them.

In Lesson 10.2, students explore algorithmic bias, learning how automated systems can unintentionally favor or disadvantage certain groups. Through real-world case studies, code reasoning, and ethical discussion, students analyze fairness in systems they encounter every day.

This lesson is part of Unit 10: Data, Privacy & Ethical Computing and helps students critically examine the social impact of computing while building responsible decision-making skills.

✅ What’s Included

✔ Student Worksheet

• Guided notes on algorithms and bias
  
• Vocabulary matching
  
• Real-world case studies (bias identification)
  
• Code analysis & ethical reasoning (Python-style)
  
• JDoodle coding task with challenge extension
  
• Reflection prompts (Answer ONE format)
  

✔ Teacher Guide

• Lesson overview & suggested pacing (50–75 minutes)
  
• Common misconceptions about algorithms and bias
  
• Complete answer key
  
• Sample JDoodle solution
  
• Differentiation & extension ideas
  

🧠 Topics Covered

• Algorithms
  
• Bias & algorithmic bias
  
• Fairness in automated systems
  
• Data sets and representation
  
• Ethical implications of computing
  

🧑‍🏫 Perfect For

• High school Computer Science Principles (Grades 9–12)
  
• Python-based CSP courses
  
• Data ethics & algorithm fairness units
  
• Discussion-heavy lessons
  
• Sub plans and low-prep days