Unit 5: Ethics and Bias

Lesson 2: Fairness and Bias Detection (1 hour)

Lesson content from Unit 5: Ethics and Bias

Lesson 2: Fairness and Bias Detection (1 hour)

Learning Objectives

  • Understand different definitions of fairness
  • Learn how to detect bias in AI systems
  • Use tools to analyze bias in AI applications
  • Understand challenges in achieving fairness

Materials Needed

  • Internet-connected devices
  • Bias detection tools and examples
  • Case studies
  • Student notebooks
  • Examples of biased vs. fair systems

Time Breakdown

  • Review bias concepts (5 min)
  • Understanding fairness (15 min)
  • How to detect bias (15 min)
  • Hands-on: Bias detection activities (20 min)
  • Wrap-up (5 min)

Activities

1. Review Bias Concepts (5 min)

  • What is bias?
  • How does bias get into AI?
  • Share homework examples
  • Bridge: "Today we'll learn how to detect and measure bias"

2. Understanding Fairness (15 min)

What is Fairness?

  • Treating people equally
  • Not discriminating based on protected characteristics
  • Equal opportunity
  • But: Fairness can be defined in different ways

Different Definitions of Fairness:

1. Demographic Parity:

  • Equal outcomes for different groups
  • Example: Hiring rate same for all groups
  • Challenge: Groups might have different qualifications

2. Equalized Odds:

  • Equal accuracy for all groups
  • Example: Face recognition equally accurate for all
  • Challenge: Might require different thresholds

3. Individual Fairness:

  • Similar people treated similarly
  • Example: People with similar qualifications get similar treatment
  • Challenge: Defining "similar"

The Fairness Trade-off:

  • Different definitions can conflict
  • Can't always optimize for all types of fairness
  • Requires trade-offs and value judgments
  • Example: Accuracy vs. fairness

Real-World Challenges:

Challenge 1: Defining Protected Groups

  • Gender, race, age, disability, etc.
  • But: Intersectionality (multiple identities)
  • Example: Black women face different bias than white women or black men

Challenge 2: Proxy Variables

  • AI might use zip code (proxy for race)
  • Or: Name, school, etc.
  • Hard to detect indirect discrimination

Challenge 3: Historical Bias

  • Historical data reflects past discrimination
  • Even "fair" algorithms trained on biased data are biased
  • Need to account for historical context

Discussion:

  • What does fairness mean to you?
  • Can AI ever be completely fair?
  • Who should decide what's fair?

3. How to Detect Bias (15 min)

Methods for Detecting Bias:

1. Data Analysis:

  • Check training data for representation
  • Are all groups represented equally?
  • Are there stereotypes in the data?
  • Example: Image dataset with mostly white faces

2. Performance Analysis:

  • Test accuracy for different groups
  • Compare error rates
  • Example: Face recognition accuracy by race/gender

3. Output Analysis:

  • Analyze system outputs for patterns
  • Do certain groups get different results?
  • Example: Job recommendations by gender

4. User Testing:

  • Test with diverse users
  • Gather feedback
  • Identify problems in real use

Key Questions to Ask:

  • Who is the system designed for?
  • Who is in the training data?
  • Who might be excluded or harmed?
  • How does it perform for different groups?
  • What are the potential biases?

Red Flags:

  • System only tested on one group
  • Training data not diverse
  • No consideration of fairness
  • No monitoring of bias
  • No diverse team developing it

4. Hands-On: Bias Detection Activities (20 min)

Activity 1: Analyzing Image Datasets (7 min)

  • Show examples of image datasets
  • Students analyze:
    • Who is represented? (gender, race, age)
    • What activities are shown?
    • Are there stereotypes?
    • What groups might be missing?
  • Discuss findings

Activity 2: Testing Recommendation Systems (7 min)

  • Students test recommendation systems (YouTube, Netflix, etc.)
  • Compare recommendations:
    • Different user profiles
    • Different search histories
    • Different demographics (if possible)
  • Analyze: Are recommendations biased? How?

Activity 3: Bias Detection in News/Search (6 min)

  • Students search for topics from different perspectives
  • Compare results:
    • Different search terms
    • Different user contexts
    • Analyze: Are results biased? How?
  • Example: Search "CEO" images - what appears?

Reflection Questions:

  • What biases did you find?
  • Why might these biases exist?
  • Who might be affected?
  • How could these be addressed?

5. Wrap-Up (5 min)

Key Takeaways:

  • Fairness can be defined in different ways
  • Multiple methods to detect bias
  • Important to test with diverse groups
  • Bias detection requires ongoing effort

Next Steps:

  • Be critical consumers of AI
  • Question systems you use
  • Advocate for fairness
  • Preview: Next lesson - Privacy and surveillance

Differentiation Strategies

  • Younger students: Focus on simple examples, guided activities, age-appropriate discussions
  • Older students: Explore fairness metrics mathematically, analyze case studies in depth, research detection methods
  • Struggling learners: Use more structured activities, simpler examples, more guidance
  • Advanced learners: Research specific fairness algorithms, explore trade-offs mathematically, analyze policy implications

Assessment

  • Participation in bias detection activities
  • Quality of analysis and observations
  • Understanding of fairness concepts
  • Reflection journal entry