AP Computer Science A: Lesson 4.1

Ethical & Social Issues Around Data Collection

Unit 4: Data Collections

This notebook serves as a complete lesson plan, presentation guide, and homework assignment for Topic 4.1.

🖥️ Presentation & Slide Deck

Slide 1: Welcome & Warm-Up

  • Title: Data Collection: Bias, Privacy, and Social Impact
  • Warm-Up Prompt: “If a programmer writes 100% bug-free Java code, can the program still cause real-world harm? Why or why not?”

🗣️ Teacher Script:

“Welcome everyone! Today we are taking a pause from code syntax to talk about something just as critical: Data Set Fitness. As software engineers, your algorithms are only as unbiased as the data you feed them. A perfectly written loop processing flawed data will produce flawed, and often harmful, real-world decisions.”

Slide 2: Core Concepts

  • Privacy & Data Ownership: Who owns user data once collected? What risks exist when sensitive data is breached or sold?
  • Algorithmic Bias: Automated systems in hiring, lending, and law enforcement reinforcing historical biases.
  • Data Set Fitness: Using incomplete, unrepresentative, or skewed datasets leads to incorrect conclusions.

🗣️ Teacher Script:

“When we talk about ‘fitness’ in computer science, we mean: Is this data suitable for the purpose of this algorithm? If you train a model on data that only represents a subset of the population, your algorithm will fail when applied to everyone else.”

Slide 3: Interactive Case Study — “The Flawed Fitness App”

Scenario: A tech company launches an AI health app to predict cardiac risk. The dataset used to train the algorithm consisted entirely of 20-something male professional athletes.

Class Discussion Questions:

  1. What happens when this software evaluates an elderly patient or a female patient?
  2. Where did the failure occur—in the Java logic, or in the data collection phase?
  3. Who is legally and ethically responsible for this mistake?

📝 In-Class Socratic Discussion Activity

Group Exercise (15 mins): Break into pairs and discuss the following scenario:

A university uses an automated screening system to rank applicants. The algorithm was trained on historic admissions data from the past 30 years.

Prompts:

  • How might historical bias be embedded in past admissions decisions?
  • How can developers audit the dataset to ensure equity before deploying the model?

📄 Student Homework Assignment Handout

Title: Real-World Algorithmic Bias Research & Reflection

Instructions: Research a documented, real-world case of algorithmic bias (e.g., facial recognition inaccuracies across demographic groups, automated hiring tools, or predictive policing tools). Write a 300-word reflection answering the following questions:

Homework Reflection Template

  1. System Background: What was the intended purpose of the software tool?
  2. Root Cause Analysis: How did flawed data collection or unrepresentative training data cause the system to fail?
  3. Developer Response: If you were the lead Java developer on this project, what specific changes would you make to the data collection process to eliminate this bias?
# Write your homework reflection below in plain text or markdown comments:
'''
Student Name: 
Date: 
Case Study Chosen: 

1. System Background:


2. Root Cause Analysis:


3. Developer Response:
'''