Unit 5: Ethics and Bias

Lesson 1: Introduction to AI Ethics and Bias (1 hour)

Lesson content from Unit 5: Ethics and Bias

Lesson 1: Introduction to AI Ethics and Bias (1 hour)

Learning Objectives

  • Understand what ethics means in the context of AI
  • Define bias and recognize it in everyday life
  • Understand how bias can appear in AI systems
  • Identify why bias in AI is particularly concerning

Materials Needed

  • Whiteboard or digital presentation
  • Examples of bias (everyday and AI)
  • Case studies of AI bias
  • Student notebooks
  • Internet connection for examples

Time Breakdown

  • Introduction: What is ethics? (10 min)
  • Understanding bias (15 min)
  • How bias appears in AI (20 min)
  • Why bias matters (10 min)
  • Wrap-up and homework (5 min)

Activities

1. Introduction: What is Ethics? (10 min)

Definition:

  • Ethics: Moral principles that govern behavior
  • What is right vs. wrong
  • How we should treat others
  • What responsibilities we have

Ethics in Technology:

  • How should we use technology?
  • What should we build?
  • Who benefits? Who might be harmed?
  • What are our responsibilities?

AI Ethics Questions:

  • Should AI be used for surveillance?
  • Who is responsible when AI makes mistakes?
  • Should AI replace human jobs?
  • How do we ensure AI is fair?
  • Who gets access to AI technology?

Why Ethics Matters:

  • AI affects real people's lives
  • AI decisions can be harmful
  • AI can amplify existing problems
  • We have responsibility to use AI wisely

Discussion:

  • What ethical questions do you have about AI?
  • Have you seen AI used in ways that concern you?
  • What responsibilities do AI developers have?

2. Understanding Bias (15 min)

What is Bias?

  • Prejudice in favor of or against something
  • Unfair treatment based on certain characteristics
  • Can be conscious or unconscious
  • Can be individual or systemic

Examples of Bias in Everyday Life:

  • Hiring: Preferring certain groups
  • Lending: Denying loans to certain groups
  • Media: Representing groups in certain ways
  • Education: Treating students differently

Types of Bias:

  • Gender bias: Treating people differently based on gender
  • Racial bias: Treating people differently based on race
  • Age bias: Treating people differently based on age
  • Socioeconomic bias: Treating people differently based on income/class
  • Cultural bias: Favoring certain cultures over others

Key Insight:

  • Bias exists in human society
  • AI systems learn from human data
  • If data contains bias, AI learns bias
  • AI can amplify bias at scale

3. How Bias Appears in AI (20 min)

How Bias Gets into AI:

1. Biased Data:

  • Training data reflects real-world biases
  • Historical discrimination becomes encoded
  • Example: Hiring data biased against certain groups
  • AI learns and reproduces these patterns

2. Biased Designers:

  • Developers' unconscious biases influence design
  • Choices about what to optimize for
  • What problems to solve, who to serve
  • Example: Face recognition designed/tested mainly on one group

3. Biased Algorithms:

  • Algorithms might favor certain outcomes
  • Optimization goals might be unfair
  • Example: Optimizing for profit vs. fairness

4. Biased Feedback:

  • User feedback can be biased
  • Reinforcement learning from biased users
  • Example: Recommendation systems amplifying stereotypes

Real-World Examples:

Example 1: Facial Recognition

  • Lower accuracy for people of color, women
  • Trained on mostly white male faces
  • Consequences: Wrongful arrests, surveillance issues

Example 2: Hiring Algorithms

  • Discriminated against women in tech
  • Learned from historical hiring data
  • Penalized resumes with "women's" activities/words

Example 3: Loan Approval

  • Discriminated against certain groups
  • Used zip codes (proxy for race)
  • Denied loans unfairly

Example 4: Search Results

  • Job searches showing different results by gender
  • Reinforcing stereotypes
  • Limiting opportunities

Discussion:

  • Why do you think these biases happened?
  • What are the consequences?
  • How could they be prevented?

4. Why Bias Matters (10 min)

Consequences of AI Bias:

Individual Harm:

  • Unfair treatment
  • Missed opportunities
  • Wrongful decisions
  • Privacy violations

Societal Harm:

  • Reinforces discrimination
  • Amplifies inequality
  • Undermines trust in technology
  • Perpetuates injustice

Scale Problem:

  • Human bias: Affects individuals
  • AI bias: Can affect millions instantly
  • Automated decisions at scale
  • Hard to detect and fix

Why It's Hard to Fix:

  • Bias can be subtle
  • Not always intentional
  • Requires diverse teams
  • Requires ongoing monitoring

Our Responsibility:

  • As users: Be aware, question decisions
  • As creators: Build fair systems
  • As citizens: Advocate for fairness
  • As learners: Understand and address bias

5. Wrap-Up and Homework (5 min)

Key Takeaways:

  • Ethics: How we should use AI responsibly
  • Bias: Unfair treatment, can appear in AI
  • AI learns bias from data, designers, algorithms
  • Bias in AI can harm individuals and society
  • We all have responsibility to address bias

Homework:

  • Find one example of potential AI bias in your daily life
  • Could be: Search results, recommendations, apps
  • Write: What bias might exist? Who might be affected?
  • Be ready to share next class

Preview: Next lesson - We'll explore fairness and how to detect bias in AI systems

Differentiation Strategies

  • Younger students: Focus on concrete examples, simpler explanations, age-appropriate discussions
  • Older students: Explore more complex ethical theories, analyze case studies in depth, research specific incidents
  • Struggling learners: Use more examples, simpler concepts, more guidance
  • Advanced learners: Research specific bias incidents, explore fairness metrics, analyze root causes

Assessment

  • Participation in discussion
  • Understanding of bias concepts
  • Quality of homework examples
  • Reflection journal entry