Weekly Readings on AI and Society Course

Week 1: Fundamentals of AI

Tue, 05 May 2026 00:00:00 +0000

Week 1: Fundamentals of AI

Focus: What is AI/ML? Brief history and overview

📚 Required Readings

Primary Readings

“Artificial Intelligence: A Brief History” (15 min read)
- Overview of AI development from symbolic AI to modern machine learning
- Key figures and pivotal moments in AI research
“What is Machine Learning?” (20 min read)
- Difference between AI and machine learning
- Supervised vs. unsupervised learning basics
- Real-world examples of ML applications

Supplementary Resources

Stanford Encyclopedia of Philosophy: Artificial Intelligence (link) — Philosophical foundations
“A Very Short Introduction to Artificial Intelligence” — Excerpts (10 min)

💭 Discussion Prompts

Before class, consider these questions as you read:

Week 2: AI Systems & Data

Tue, 05 May 2026 00:00:00 +0000

Week 2: AI Systems & Data

Focus: How AI systems work; data and training

📚 Required Readings

Primary Readings

“How Machine Learning Works: The Training Process” (25 min)
- Training data, features, and labels
- Algorithms and parameter optimization
- Testing and validation
“Data: The Fuel of AI” (20 min)
- Data collection and preparation
- Data quality and representation
- The role of big data in modern AI

Supplementary Resources

“The Hidden Technical Debt in Machine Learning Systems” — Selected sections
Interactive visualizations: Neural network playground

💭 Discussion Prompts

Why is data quality more important than data quantity?
How do training decisions affect AI system behavior in the real world?
What role does feedback play in AI systems over time?

📝 Preparation for Class

Find a dataset related to a topic you care about (e.g., Kaggle, UCI Machine Learning Repository)
Reflect on what patterns you’d hope an AI system would find in that data
Consider: What could go wrong if the data was biased or incomplete?

See Week 2 Assignment for this week’s task.

Week 3: Ethics & Bias

Tue, 05 May 2026 00:00:00 +0000

Week 3: Ethics & Bias

Focus: Algorithmic bias and fairness

📚 Required Readings

Primary Readings

“Weapons of Math Destruction” — Chapter 1 (30 min)
- How algorithms can amplify inequality
- Case studies of biased AI systems
- The impact on vulnerable communities
“Defining and Detecting Algorithmic Bias” (25 min)
- Statistical definitions of fairness
- Types of bias: historical, measurement, representation, evaluation
- Methods for detecting and mitigating bias

Supplementary Resources

ProPublica: “Machine Bias” investigation — Interactive article
“The Ethics of Artificial Intelligence” — Stanford Encyclopedia excerpt

💭 Discussion Prompts

How can an AI system be mathematically “accurate” yet fundamentally unfair?
When is some level of bias acceptable in AI systems?
Who should be responsible for the harms caused by biased algorithms?

📝 Preparation for Class

Read about a real-world case of algorithmic bias (e.g., Amazon hiring, COMPAS, facial recognition)
Prepare a 2-minute summary: What went wrong? Who was harmed?
Brainstorm: How could that system have been designed more fairly?

See Week 3 Assignment for this week’s task.

Week 4: Policy & Governance

Tue, 05 May 2026 00:00:00 +0000

Week 4: Policy & Governance

Focus: Regulation and policy approaches

📚 Required Readings

Primary Readings

“Global AI Governance Approaches” (30 min)
- EU AI Act overview
- US regulatory landscape
- Approaches from China, Canada, and other regions
- Key principles: transparency, accountability, human control
“Regulating AI: The Challenge of Speed and Change” (25 min)
- Why traditional regulation struggles with AI
- Policy options: principles-based vs. rules-based regulation
- Role of self-regulation, industry standards, and technical safety

Supplementary Resources

Summary of the EU AI Act
NIST AI Risk Management Framework overview
Stanford AI Index 2024 — Policy section

💭 Discussion Prompts

What are the tradeoffs between innovation and regulation?
Should AI regulation be global or local? Why?
How can policymakers stay informed about rapidly evolving technology?

📝 Preparation for Class

Choose one recent AI policy or regulation (EU AI Act, Biden Executive Order, etc.)
Prepare a 2-minute summary: What does it do? What problems does it aim to solve?
Consider: Is this approach effective? What are its limitations?

See Week 4 Assignment for this week’s task.

Week 5: Employment & Economics

Tue, 05 May 2026 00:00:00 +0000

Week 5: Employment & Economics

Focus: Job displacement, economic implications

📚 Required Readings

Primary Readings

“AI and the Future of Work” (30 min)
- Historical context: previous waves of technological disruption
- Which jobs are most vulnerable to automation?
- Retraining and reskilling: what has worked before?
“Economic Inequality and AI” (25 min)
- Winner-take-all dynamics in AI markets
- Wealth concentration and market power
- Policy responses: UBI, wage subsidies, taxation approaches

Supplementary Resources

McKinsey: “The Future of Work After COVID-19” — Selected sections
“Race After Technology” by Ruha Benjamin — Chapter on economics

💭 Discussion Prompts

What economic sectors are most at risk from AI automation in the next 10 years?
How might society distribute the economic gains from AI productivity?
Is job displacement from AI different from job loss from other technologies? How?

📝 Preparation for Class

Research one occupation you’re interested in: How might AI change that field?
Find 2-3 policy proposals addressing AI and employment
Reflect: Which approach(es) seem most promising to you? Why?

See Week 5 Assignment for this week’s task.

Week 6: AI in Society

Tue, 05 May 2026 00:00:00 +0000

Week 6: AI in Society

Focus: Applications across domains

📚 Required Readings

Primary Readings

“AI in Healthcare: Promises and Pitfalls” (25 min)
- Diagnostic AI systems
- Drug discovery and personalized medicine
- Equity in healthcare AI applications
“AI in Criminal Justice: Beyond Predictive Policing” (25 min)
- Risk assessment algorithms in sentencing
- Bias in criminal justice AI systems
- Transparency and accountability challenges

Supplementary Resources

“AI in Education: Opportunities and Risks” — Brief overview
Case studies from your field of interest

💭 Discussion Prompts

What are the unique ethical considerations in high-stakes domains like healthcare or criminal justice?
How do we balance innovation benefits with preventing harm?
Who should benefit most from AI applications?

📝 Preparation for Class

Choose one domain where AI is being deployed (healthcare, education, transportation, finance, etc.)
Research: What does the AI system do? What problem does it solve?
Analyze: What are the potential benefits and harms for different groups?

See Week 6 Assignment for this week’s task.

Week 7: Midterm Review & Synthesis

Tue, 05 May 2026 00:00:00 +0000

Week 7: Midterm Review & Synthesis

Focus: Integration of Weeks 1-6 concepts

📚 Review Readings

Key Concepts to Review

AI Fundamentals (Weeks 1-2)
- What is AI/ML? How systems are trained.
- Key terminology and basic processes
Ethics and Governance (Weeks 3-4)
- Bias and fairness definitions
- Policy approaches and regulatory frameworks
Societal Impact (Weeks 5-6)
- Economic implications and employment
- Domain-specific applications

Study Materials

Glossary of key terms
Summary charts and timelines
Review questions from previous weeks

💭 Reflection Questions

How have your views on AI and society evolved over the past 6 weeks?
What are the most important ethical challenges we face?
How do technical, policy, and societal factors interconnect?

📝 Midterm Preparation

Review all weekly reading discussion prompts
Revisit case studies from weeks 1-6
Prepare examples to illustrate key concepts
Formulate your own perspective on AI and society issues

See Midterm Assessment for details on the midterm exam format and expectations.

Week 8: Case Studies

Tue, 05 May 2026 00:00:00 +0000

Week 8: Case Studies

Focus: Deep dives into real-world examples

📚 Case Study Readings

Featured Case Studies

“The COMPAS Recidivism Algorithm” (20 min)
- Predictive policing and criminal justice
- ProPublica investigation
- Questions of bias and accountability
“Amazon’s Hiring Algorithm” (20 min)
- Gender bias in recruitment AI
- Training data issues
- Lessons learned and corrections

Supplementary Cases

Facial recognition systems and civil liberties
Social media algorithms and misinformation
ChatGPT and generative AI capabilities/limitations
Select your own case study from current news

💭 Analysis Framework

For each case study, consider:

Week 9: Emerging Issues

Tue, 05 May 2026 00:00:00 +0000

Week 9: Emerging Issues

Focus: Privacy, surveillance, generative AI

📚 Required Readings

Primary Readings

“Privacy in the Age of AI” (25 min)
- Data collection and personal privacy
- Differential privacy and technical solutions
- Right to privacy in different jurisdictions
“Surveillance Capitalism” — Chapter selections (25 min)
- Business models based on data extraction
- Power asymmetries between individuals and platforms
- Societal implications
“Generative AI: Opportunities and Risks” (25 min)
- How large language models work
- Applications and capabilities
- Concerns: bias, misinformation, environmental costs, training data ethics

Supplementary Resources

Facial recognition and mass surveillance — Current articles
Copyright and AI training data — Legal perspectives
“The Environmental Costs of AI” — Brief overview

💭 Discussion Prompts

Is privacy still possible in a world of ubiquitous data collection?
What responsibility do generative AI creators have for training data and outputs?
How should we balance innovation in AI with protection of fundamental rights?

📝 Preparation for Class

Reflect on your own digital privacy: What data about you exists? Who has access?
Try a generative AI tool (ChatGPT, Claude, etc.): What can it do? What are its limitations?
Research one recent AI privacy or surveillance controversy

See Week 9 Assignment for this week’s task.

Week 10: Autonomous Systems

Tue, 05 May 2026 00:00:00 +0000

Week 10: Autonomous Systems

Focus: Self-driving cars, robotics, accountability

📚 Required Readings

Primary Readings

“The Ethics of Autonomous Vehicles” (30 min)
- How self-driving cars work (sensors, decision-making)
- The trolley problem and real-world tradeoffs
- Liability and insurance in autonomous systems
- Safety standards and testing protocols
“Robots in Society: Accountability and Control” (25 min)
- Where are robots deployed? What tasks?
- Human-robot interaction and trust
- Who is responsible when autonomous systems cause harm?

Supplementary Resources

Tesla autopilot incidents and investigations
Boston Dynamics robots — Current capabilities and concerns
“Why Accountability Matters for Autonomous Weapons” — Brief overview

💭 Discussion Prompts

Who should be held liable when an autonomous vehicle causes an accident?
How much human oversight is necessary for autonomous systems?
Should there be limits on where robots or autonomous systems can be deployed?

📝 Preparation for Class

Research one autonomous system currently in use (self-driving car, delivery robot, medical robot, etc.)
Prepare: How it works, current limitations, real-world incidents if any
Consider: What could go wrong? What safeguards exist?

See Week 10 Assignment for this week’s task.

Week 11: Futures & Regulation

Tue, 05 May 2026 00:00:00 +0000

Week 11: Futures & Regulation

Focus: Possible futures, values, and comprehensive regulation

📚 Required Readings

Primary Readings

“The Future of AI: Scenarios and Possibilities” (30 min)
- Optimistic scenarios: abundance, solutions to major challenges
- Pessimistic scenarios: misalignment, power concentration
- Most likely path: continued uncertainty and mixed outcomes
- Role of decisions we make today
“Comprehensive AI Governance Frameworks” (30 min)
- Integration of technical, policy, and ethical approaches
- Role of stakeholders: governments, industry, civil society, academia
- International cooperation challenges
- Building values into AI systems from the start

Supplementary Resources

AI policy recommendations from diverse organizations
“The Alignment Problem” — Overview of AI safety research
Scenarios from AI foresight exercises

💭 Discussion Prompts

What future for AI and society would you most want to see?
How can we ensure AI benefits are broadly shared?
What values should guide AI development and deployment?

📝 Preparation for Class

Envision your preferred future with AI (5-10 years from now): What works well? What problems have been solved or prevented?
Write down 2-3 actions or policies that would help achieve that future
Reflect on obstacles and resistance that might prevent that future

See Week 11 Assignment for this week’s task.

Week 12: Student Projects & Synthesis

Tue, 05 May 2026 00:00:00 +0000

Week 12: Student Projects & Synthesis

Focus: Student-led explorations and integration of course concepts

📚 Project Resources

Research & Writing Support

How to find credible sources on AI and society topics
Fact-checking and verification strategies for AI claims
Structure for policy briefs and research reports
Citation guidelines (Chicago Manual of Style, APA, MLA)

Presentation Skills

Presenting technical information to non-technical audiences
Visual design principles for slides
Crafting compelling arguments with evidence
Handling questions and disagreements respectfully

Topic Ideas

Students may choose from:

Weekly Readings on AI and Society Course

Week 1: Fundamentals of AI

Week 1: Fundamentals of AI

📚 Required Readings

Primary Readings

Supplementary Resources

💭 Discussion Prompts

Week 2: AI Systems & Data

Week 2: AI Systems & Data

📚 Required Readings

Primary Readings

Supplementary Resources

💭 Discussion Prompts

📝 Preparation for Class

🔗 Related Assignment

Week 3: Ethics & Bias

Week 3: Ethics & Bias

📚 Required Readings

Primary Readings

Supplementary Resources

💭 Discussion Prompts

📝 Preparation for Class

🔗 Related Assignment

Week 4: Policy & Governance

Week 4: Policy & Governance

📚 Required Readings

Primary Readings

Supplementary Resources

💭 Discussion Prompts

📝 Preparation for Class

🔗 Related Assignment

Week 5: Employment & Economics

Week 5: Employment & Economics

📚 Required Readings

Primary Readings

Supplementary Resources

💭 Discussion Prompts

📝 Preparation for Class

🔗 Related Assignment

Week 6: AI in Society

Week 6: AI in Society

📚 Required Readings

Primary Readings

Supplementary Resources

💭 Discussion Prompts

📝 Preparation for Class

🔗 Related Assignment

Week 7: Midterm Review & Synthesis

Week 7: Midterm Review & Synthesis

📚 Review Readings

Key Concepts to Review

Study Materials

💭 Reflection Questions

📝 Midterm Preparation

🔗 Related Assessment

Week 8: Case Studies

Week 8: Case Studies

📚 Case Study Readings

Featured Case Studies

Supplementary Cases

💭 Analysis Framework

Week 9: Emerging Issues

Week 9: Emerging Issues

📚 Required Readings

Primary Readings

Supplementary Resources

💭 Discussion Prompts

📝 Preparation for Class

🔗 Related Assignment

Week 10: Autonomous Systems

Week 10: Autonomous Systems

📚 Required Readings

Primary Readings

Supplementary Resources

💭 Discussion Prompts

📝 Preparation for Class

🔗 Related Assignment

Week 11: Futures & Regulation

Week 11: Futures & Regulation

📚 Required Readings