Unit 5: Ethics and Bias

Lesson 3: Privacy and Surveillance (1 hour)

Lesson content from Unit 5: Ethics and Bias

Lesson 3: Privacy and Surveillance (1 hour)

Learning Objectives

  • Understand privacy concerns related to AI
  • Recognize how AI can be used for surveillance
  • Identify data collection and usage issues
  • Discuss the balance between security and privacy

Materials Needed

  • Examples of AI surveillance
  • Privacy case studies
  • Student notebooks
  • Internet connection for research
  • Discussion prompts

Time Breakdown

  • Review fairness and bias (5 min)
  • Understanding privacy (15 min)
  • AI and surveillance (15 min)
  • Data collection and usage (15 min)
  • Discussion and wrap-up (10 min)

Activities

1. Review Fairness and Bias (5 min)

  • What is fairness?
  • How do we detect bias?
  • Bridge: "Today we'll explore another ethical concern: privacy"

2. Understanding Privacy (15 min)

What is Privacy?

  • Right to control information about yourself
  • Right to be left alone
  • Freedom from surveillance
  • Control over personal data

Why Privacy Matters:

  • Personal freedom
  • Protection from harm
  • Right to autonomy
  • Foundation of democracy

Privacy in the Digital Age:

  • More data collected than ever
  • Often collected without clear consent
  • Used in ways we don't expect
  • Hard to control once shared

Types of Privacy:

  • Information privacy: Control over personal data
  • Location privacy: Control over location tracking
  • Communication privacy: Control over communications
  • Behavioral privacy: Control over behavior tracking

AI and Privacy Concerns:

  • AI systems need data to work
  • Often need personal data
  • Can infer sensitive information
  • Can track and profile individuals

3. AI and Surveillance (15 min)

How AI Enables Surveillance:

1. Facial Recognition:

  • Identify individuals in public spaces
  • Track movements
  • Monitor behavior
  • Concerns: Mass surveillance, tracking, false positives

2. Behavior Analysis:

  • Analyze behavior patterns
  • Predict actions
  • Identify "suspicious" behavior
  • Concerns: Profiling, discrimination, false positives

3. Social Media Monitoring:

  • Analyze posts, comments, connections
  • Identify interests, beliefs, associations
  • Predict behavior
  • Concerns: Privacy invasion, manipulation

4. Location Tracking:

  • GPS, cell towers, WiFi
  • Track movements over time
  • Predict destinations
  • Concerns: Stalking, surveillance, privacy

Real-World Examples:

Example 1: Smart Cities

  • Cameras everywhere
  • Facial recognition
  • Behavior tracking
  • Benefits: Crime prevention, traffic management
  • Concerns: Mass surveillance, privacy loss

Example 2: Social Credit Systems

  • Monitor behavior
  • Score individuals
  • Reward/punish based on score
  • Benefits: Encourages good behavior
  • Concerns: Control, discrimination, privacy

Example 3: Workplace Monitoring

  • Monitor employee behavior
  • Analyze productivity
  • Track locations, communications
  • Benefits: Efficiency, safety
  • Concerns: Privacy, trust, autonomy

Example 4: Predictive Policing

  • Predict where crime will occur
  • Identify "likely" criminals
  • Allocate police resources
  • Benefits: Crime prevention
  • Concerns: Bias, discrimination, privacy

Discussion:

  • Where is the line between security and privacy?
  • When is surveillance acceptable?
  • Who should have access to surveillance data?
  • How do we balance benefits and concerns?

4. Data Collection and Usage (15 min)

How Data is Collected:

  • Explicit: Forms, surveys, accounts
  • Implicit: Browsing, clicks, location
  • Purchased: Data brokers, third parties
  • Inferred: AI predicts missing data

What Data is Collected:

  • Personal information (name, age, address)
  • Behavioral data (clicks, purchases, searches)
  • Location data (GPS, check-ins)
  • Biometric data (face, voice, fingerprint)
  • Social data (connections, interactions)

How Data is Used:

  • Training AI models
  • Personalization (recommendations, ads)
  • Decision-making (loans, jobs, insurance)
  • Profiling and targeting
  • Surveillance and monitoring

Concerns:

1. Consent:

  • Do users understand what they're agreeing to?
  • Can they really consent to complex data usage?
  • Is consent meaningful if they can't opt out?

2. Purpose Creep:

  • Data collected for one purpose, used for another
  • Example: Health data used for insurance

3. Data Breaches:

  • Personal data stolen
  • Used for identity theft, fraud
  • Once leaked, can't be undone

4. Inference:

  • AI can infer sensitive information
  • Example: Predict health from shopping data
  • Privacy lost even without explicit data

5. Lack of Control:

  • Hard to know what data is collected
  • Hard to delete data
  • Hard to control how it's used

Discussion:

  • What data are you comfortable sharing?
  • What should be private?
  • How can we protect privacy?
  • What rights should we have?

5. Discussion and Wrap-Up (10 min)

Key Takeaways:

  • Privacy: Right to control personal information
  • AI enables new forms of surveillance
  • Data collection raises many concerns
  • Need to balance benefits and privacy

Ethical Questions:

  • Who should have access to surveillance?
  • How do we protect privacy while benefiting from AI?
  • What regulations are needed?
  • What can individuals do?

Our Responsibility:

  • Be aware of data collection
  • Make informed choices
  • Advocate for privacy rights
  • Support responsible data use

Preview: Next lesson - Job displacement and economic impacts

Differentiation Strategies

  • Younger students: Focus on age-appropriate examples, simpler concepts, guided discussion
  • Older students: Explore legal frameworks, analyze case studies, research specific surveillance systems
  • Struggling learners: Use more examples, simpler explanations, more structure
  • Advanced learners: Research privacy laws, explore technical privacy solutions, analyze policy implications

Assessment

  • Participation in discussion
  • Understanding of privacy concepts
  • Quality of analysis
  • Reflection journal entry