AP CSP Day 13 - Bias in Data

Course Information

• Course: AP Computer Science Principles
• Unit: Big Idea 3 - Data & Information (DAT)
• Lesson: Day 13 (50 minutes)
• Learning Objective: DAT-1.I - Explain bias in data collection and processing

Learning Objectives

Primary Goals

Students will be able to:

1. Define bias in data
2. Identify common sources of bias
3. Apply techniques to reduce bias
4. Analyze the impact of bias on decision-making

AP Exam Alignment

• Big Idea 3: Data & Information (27-36% of AP Exam)
• Essential Knowledge: DAT-1.I.1, DAT-1.I.2, DAT-1.I.3
• Computational Thinking Practice: 3.F - Identify and address bias in data

Lesson Structure (50 minutes)

Opening Hook (10 minutes)

13.1 Welcome & Lesson Preview (5 minutes)

Teacher Activities:

• Welcome students to Day 13
• Review previous lesson's key concepts
• Introduce today's focus on bias in data

Student Activities:

• Review previous lesson's materials
• Think about: "What is bias in data?"

13.2 Quick Bias Challenge (5 minutes)

Activity: "Identify Bias"
Instructions:

• Groups of 4-6 students
• Identify bias in a given dataset
• Discuss the process

Purpose: Activate thinking about data bias

Core Content Instruction (20 minutes)

14.1 What is Bias in Data? (10 minutes)

Definition (DAT-1.I.1):

Bias in data occurs when certain groups or perspectives are overrepresented or underrepresented.

Key Concepts:

• **Sampling bias: Unequal representation in datasets
• **Algorithmic bias: Unfair outcomes from algorithms
• **Data preprocessing: Techniques to reduce bias

Case Study: The bias in hiring algorithms

• Bias process: Identifying sampling and algorithmic bias
• Outcome: Fairer hiring decisions through preprocessing

14.2 Common Sources of Bias (5 minutes)

Sources:

• **Sampling: Unequal representation
• **Algorithm design: Flawed logic
• **Data preprocessing: Incomplete cleaning

Examples:

• Hiring app: Sampling bias in candidate selection
• Loan approval system: Algorithmic bias in decision-making

14.3 Importance of Reducing Bias (5 minutes)

Why is it important?:

• Fairness: Ensuring equal representation
• Accuracy: More reliable insights
• Ethics: Avoiding discriminatory outcomes

Discussion Questions:

1. How does sampling bias affect datasets?
2. What are the consequences of algorithmic bias?
3. Why is reducing bias important for ethical computing?

Hands-On Activity (15 minutes)

15.1 Group Project: Bias Reduction Practice (15 minutes)

Activity: "Reduce Bias in a Dataset"
Instructions:

• Groups of 3-4 students
• Identify and reduce bias in datasets
• Discuss the process and its applications
• Present findings

Materials:

• **List of datasets with bias
• **Bias reduction worksheet
• **Access to coding environment

Learning Goals:

• **Understand data bias
• **Identify bias sources
• **Apply bias reduction techniques
• **Present ideas effectively

Assessment:

• **Group participation
• **Bias identification accuracy
• **Use of bias reduction tools
• **Presentation clarity

Closure & Preview (5 minutes)

16.1 Key Concepts Review (2 minutes)

Today's Learning Highlights:

1. ✅ Understanding data bias
2. ✅ Identifying bias sources
3. ✅ Applying bias reduction techniques
4. ✅ Analyzing bias impact

AP Exam Connection:

• These concepts will appear in AP exam multiple choice questions
• Understanding bias is crucial for the Explore Performance Task

16.2 Next Class Preview (3 minutes)

Day 14 Topic: "Data Project - Day 1"

• Learning Objective: DAT-1.J - Develop a data project
• Activity: Practicing data project development techniques
• Homework: Think about a recent program you used. What biases did it have? How could they be reduced?