Bias Busters Lesson PlanBias Busters Plan 5th Grade Digital Literacy Duration 45 MINS Objective Students will define algorithmic bias, identify unfair AI examples, and propose fairness improvements through collaborative analysis and reflection. The "Why" Understanding algorithmic bias enhances critical thinking and digital literacy, empowering students to recognize and challenge unfairness in technology. Prep Checklist Review Fair or Foul AI slide deck Print copies of Algorithm Stories Gather sticky notes, chart paper, and markers Set up projector for the slide deck Materials Needed Slide Deck: Fair or Foul AI Worksheet: Algorithm Stories Sticky Notes & Markers Large Chart Paper Instructional Steps 1 Warm-Up: Fair or Foul AI 5 MINS Display sample AI scenarios from the Fair or Foul AI slide deck. Ask students to vote whether each outcome is fair or foul (thumbs up/down). Invite 2–3 students to share quick reasons for their votes. 2 Group Exploration: Algorithm Stories 15 MINS Divide students into small groups of 3–4. Provide each group with the Algorithm Stories reading packet. Instruct groups to read and highlight examples of bias in the text. Have students record each specific bias example on a sticky note. 3 Discussion: Fairness Forum 15 MINS Reconvene whole class and display large chart paper. Invite each group to place their sticky-note examples on the paper. Facilitate discussion using the Fairness Forum guide: Why did this bias occur? What impact does it have? Record student ideas for solutions around the chart with markers. 4 Assessment: Bias Fix Proposals 10 MINS Ask each student to write a brief reflection (can be done on the back of the worksheet): Identify one specific bias they observed today. Provide one creative suggestion to make that AI tool fairer. Collect reflections to assess understanding and progress.
Fair or Foul AI SlidesFair or Foul AI Exploring Fairness in a Digital World Bias Busters Series Lesson 1: Spotting Unfairness What Is Algorithmic Bias? Algorithms are sets of instructions that help computers make decisions. Bias happens when these instructions treat some people unfairly. We want to spot bias and learn how to fix it! Why Fairness Matters Unfair AI can hurt feelings or stop people from getting the help they need. Fair AI treats everyone equally, no matter their background or appearance. We all deserve tools that are honest and fair. Case Study 01 Face Recognition Fun? A new app uses a camera to identify classmates' faces. It works well on some students but fails more often on students with darker skin, showing the wrong name. Fair Foul Is the camera "seeing" everyone the same way? Case Study 02 Playtime Preference An AI suggests activities at recess. It recommends basketball for boys and dance for girls, even when their actual interests are different. Fair Foul Is the AI following old-fashioned stereotypes? Case Study 03 Homework Helper An AI divides chores and study tasks. It gives harder assignments to students from certain neighborhoods, assuming they need more "discipline." Fair Foul Does where someone lives change what they deserve? Key Takeaways AI can copy human bias if we are not careful about what it learns. Spotting unfairness helps us ask better questions about technology. We make AI fairer by using better data and listening to everyone. Let's be bias busters!
Algorithm Stories ReadingAlgorithm Stories Student Name Date In these short stories, you’ll read about different algorithms—special computer instructions that help make decisions. As you read, look for examples of bias and think about how to make each algorithm fairer. Story 1: The Book Buddy Algorithm Every morning, Maya logs into her reading app called Book Buddy. It recommends new books based on what it thinks she likes. Maya notices it always suggests mystery books written by authors from one country. She loves adventure stories from around the world, but the app never shows them. Why does this happen? Book Buddy learned from a small set of reading lists that mostly featured authors from one place. Because of this, it keeps suggesting similar books and ignores diverse voices Maya might enjoy. Story 2: The Quiz Wizard Leo uses Quiz Wizard to practice math and reading. One day, he tries the reading section about everyday life situations. The app asks questions about things like dialing rotary phones or mailing letters—things most kids today don’t know. Why does this happen? The algorithm was trained on old textbooks, so it forgets that technology and daily life change. Leo feels confused and frustrated because the questions don’t match his world. Story 3: The Sports Coach App Jamal and Sofia both try the Sports Coach App to find new games. Jamal sees recommendations for basketball, football, and skateboarding. Sofia sees tennis, ballet, and swimming. Neither app asked them what they liked first. Why does this happen? The algorithm used past data showing boys played certain sports and girls played others. By following these patterns, it assumes what each child will enjoy without asking them directly. Reflection Questions 1. Name one bias you noticed in one of the stories. 2. Why could this bias be unfair or harmful to someone? 3. What is one idea you have to make the algorithm fairer? 4. Which story did you relate to the most, and why? Use your answers in our Fairness Forum to share examples and solutions with classmates.
Fairness Forum Facilitation GuideFairness Forum In this whole‐class discussion, students share examples of bias they discovered and work together to propose and critique solutions. Purpose Celebrate discoveries from the Algorithm Stories worksheet. Deepen understanding of why bias happens and how it affects people. Brainstorm realistic ways to make AI fairer. Setup Display sticky-note chart Markers ready 15 Minutes Discussion Guidelines Listen respectfully Refer to specific stories Ask for clarity Build on each other's ideas Core Questions 1 What bias did your group find? Prompt: Why do you think it happened? Notes: __________________________________________________________________ 2 How might this bias affect real people? Prompt: Who could be hurt or left out? Notes: __________________________________________________________________ 3 What suggestion did your group make to fix it? Prompt: Could we ask users more questions or change the rules? Notes: __________________________________________________________________ 4 Which solution seems most practical? Prompt: How easy is it to test or update? Notes: __________________________________________________________________ 5 Can you think of another app that might have bias? Prompt: How would you investigate it? Notes: __________________________________________________________________ Pro-Level Facilitation Prompts "Can someone restate Maria's idea in their own words?" "What might happen if we only change the training data?" "How could we check that our fix really works?" Reflection Exit Ticket (5 Mins) Ask students to write on a new sticky note: Question 1 One new insight gained today. Question 2 One question you still have about AI fairness.
