AI and Social Structures:  A syllabus

About this syllabus

This course was designed as an offering in the new, transdisciplinary AI and Society department at the University at Buffalo.  It is a 300-level course that contributes to new "AI + x" degrees.  In its design, I wanted to not offer a singular critical social take on AI's impacts, but encourage students to explore multiple viewpoints on key debates.  Is AI racing towards superintelligence, or is it a normal technology?  Do agents have their own culture, and if so, what does that mean for us?  When can it help us learn, and when does it replace our thinking?  What are the possibilities and limitations of embrace or refusal?  I also wanted students to understand that there is not one cultural understanding of "AI"; rather, it is something that is growing up in different societies in different ways.  While the "AI" we experience as students in the US is shaped by our culture — and arguably, a narrow and particular subculture who created it — there are other concepts of "AI" that exist, and still more that are possible.  It is still early days, and a great time to be exploring this terrain.

Course description

This course explores the relationships between artificial intelligence (AI) and social structures from a social science perspective. Students will examine how social, economic, political, and cultural dynamics shape AI research, development, and diffusion.  They will also explore how AI impacts social domains. 

What are we going to learn?

By the end of this course, you will be able to…
  1.  Explain sociological concepts relating to AI, and how social scientists conduct research to develop and refine these concepts
  2. Understand how social structures shape AI development, as well as the dynamics of AI research and policy
  3. Analyze how AI can impact social structures in sociological domains of the family, relationships, education, work, law, governance, security, economics, culture, and religion
  4. Articulate main policy and social responses to the social impacts of AI

Course flow

Part I introduces foundational concepts in AI, and examines: How are social structures shaping AI?   What structural features in society are producing the version of “AI” we experience?
 
Part II is topic-focused and looks at several domains in sociology, and looks at:  How is AI shaping these social domains?
 
Part III is a synthesis that looks at the future, and policy responses that can intervene in these dynamics.
 

Readings and recommended media

The landscape is always changing, which means no reading list will ever be totally current.  Recommended reading and listening includes:

Course schedule

Part I.  How social structures shape AI
 
 
Week 1:  Aug. 24, 26                              What are social structures?
                                                                     
Objectives: Be able to define AI and key concepts and terms; be able to define social structures and key terms such as class, race and ethnicity, and gender, etc., and structural forms such as organizations, networks, markets, demographic structures
 
Readings: 
  • C. Wright Mills, “The Sociological Imagination” (1959), excerpt from ch. 1, pages 1-10
  • Henry Farrell et al (2025), “Large AI models are cultural and social technologies”, Science, doi: 10.1126/science.adt9819
  • Podcast:  “The Last Invention” podcast by Longview, Episode 1 (available on multiple podcast platforms)
  • Shuang L. Frost (2025), “Translating Chinese AI”, in Machine Decision Is Not Final: China and the History of Artificial Intelligence
                           
Week 2:  Aug. 31, Sept. 2                 Understanding what shapes AI optimism
 
Objectives:  Understand the vision of the future that AI optimists see, and identify social factors shaping it; be aware of diverse visions for positive AI futures.

Readings:   
 
Week 3:  Sept. 9                           Understanding what shapes AI pessimism           
 
Objectives:  Understand the reasons scholars are cautious around how AI is developing, including sourcing of training data and labor involved; understand existential risk concerns; understand social factors shaping AI pessimism and social movements responding to AI
 
Readings:  
 
Week 4:  Sept. 14, 16               Understanding AI agent societies
 
Objectives:  Differentiate between “AI for social science” and “the social science of AI”; understand emergent research questions about machine behavior and culture; understand the methods scientists are using to study these.
 
Readings:    

Part II.  How AI shapes social structures
 
 
Week 5:  Sept. 21, 23 Intelligence and Education
 
Objectives:  Understand the ways in which AI is and might reshape institutions and social structures of education; understand different schools of thought about what intelligence is
 
Readings:
  • Blaise Agüera y Arcas and James Manyika (2025), “AI Is Evolving — And Changing our Understanding of Intelligence”, https://www.noemamag.com/ai-is-evolving-and-changing-our-understanding-of-intelligence/
  • Ethan Mollick (2025). “Against Brain Damage”, https://www.oneusefulthing.org/p/against-brain-damage
  • Nils Gilman (2026), “The University as We Know It Is Finished”, Persuasion, https://www.persuasion.community/p/the-multiversity-is-finished

 
Week 6:  Sept. 28, 30 Families and relationships

Objectives:  Identify ways in which AI is impacting family structures and social relationships; understand how different social actors are framing these impacts.
 
Readings:  
 
Week 7:   Oct. 5, 7                       Work and the Economy
 
Objectives:  Understand projections for how AI will impact employment; become familiar with debates in the future of work literature
 
Readings:  
 
 
Week 8:  Oct. 14                          Economic and environmental implications of rapid AI buildout
 
Objectives:  Be able to contextualize the infrastructure, capital and impacts of the rapid AI buildout in historical and comparative perspective; understand different arguments about the economic implications of demand for AI and potential downturn of AI investment
 
Readings:  TBD
 
 
Week 9:   Oct. 19, 21       Art and Science
 
Objectives:  Articulate debates about how AI will impact art, culture, and human creativity; understand implications of AI for science and scientific institutions
 
Readings:  
 
Week 10:  Oct. 26, 28      Law and Governance

 
Objectives:  Identify ways in which AI may reshape legal social structures and governance practices
 
Readings:              
 
Week 11:  Nov. 2, 4                    Policing and War
 
Objectives:  Be familiar with ideas about how AI has and might reshape policing, surveillance, and warfare.
 
Readings / podcasts: 
  • Rashida Richardson et al (2019) “Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice”, NYU Law Review, https://nyulawreview.org/online-features/dirty-data-bad-predictions-how-civil-rights-violations-impact-police-data-predictive-policing-systems-and-justice/
  • Paul Scharre on how AI could transform the nature of war (2025), 80,000 hours, https://80000hours.org/podcast/episodes/paul-scharre-ai-warfare-autonomous-weapons/#transcript
  • Yuval Abraham (2024), “Lavender: The AI machine directing Israel’s bombing spree in Gaza”, https://www.972mag.com/lavender-ai-israeli-army-gaza/
 
 
Week 12:  Nov. 9, 11                Wellness, Spirituality, and Religion
 
Objectives:  Reflect on some of the ways AI is interacting with institutions and social practices relating to spirituality, health and wellness, and organized religion.
 
Readings:  
 
Part III.  Policy, social change, and the future
 
Week 13:  Nov. 16, 18                         Policy and social change
 
Objectives:  Understand big-picture policy and social change approaches at the intersection of AI and social structures.
Readings:  
Week 14:  Nov. 23                       Policy specifics
 
Objectives:  Identify and critique specific policy proposals that have been discussed at different levels.
Readings:

So those are the topics... what will we actually learn?

We’re going to work on four things: concepts, skills, facts, and context.
 
Concepts are ideas that help us describe and explain things in the world: alignment, general purpose technologies, responsible AI, AI winter, public interest technology, and so on.  We will spend time learning about them, including who came up with them and why, when they might be useful, how to explain them to others, etc.
 
Skills are things your college education should be helping you develop:  finding information, collecting and analyzing data, communicating and storytelling, teamwork — in general, things that will be useful in life and in your next job.  This is a skills-focused course, focusing on specific skills related to generative AI use.
 
Facts are things like: When was the first neural network developed?  How many CO2 emissions are really associated with a ChatGPT query?  They are empirically verifiable pieces of information that tend to answer the who, what, when, and where.  In a world with Google as well as AI tools, you obviously don't need to know all the facts all the time.  But there are a few facts that are useful to memorize and have in your pocket for when you need them. 
 
Context is broader background knowledge (of which facts are just one part).  Context may be historical or spatial. 

What are the assignments?

Assignment: In-class assignments (50% of grade)
What is it?  There will be in-class assignments every week, often worksheets or lab exercises.  These will generally be graded on effort and completion. 
 
What is the purpose of it?  The purpose of these is for you to work through concepts using different examples, and for me to be able to frequently track your learning progress.
 The lowest two in-class assignments are dropped.

 
Assignment: Event reflection (10% of grade)
What is it?  A short paper you will write after attending an event relating to AI and society.  This event can be online or in person.
 
What is the purpose of it?  The main purpose is for you to see the kinds of discussions and debates happening among a community of people interested in AI, and see the range of views and concerns people are having; to have a sense of how things are playing out right now.  The secondary purpose is for you to think critically about how events are designed and about public speaking, given that this is a part of many professional jobs.

 
Assignment: Portfolio piece (35% of grade)

What is it?  This will be a scaffolded assignment (meaning there are a few parts to it) that you work on over several weeks.  You will produce a piece of work that addresses a social dimension of AI in a medium of your choosing.  You will have feedback from the professor to develop your concept.  It is designed to accommodate multiple levels of technical ambition.
 
What is the purpose of it?  The point is for you to be engaged in your own learning process through exploring a topic you’re interested in, and for you to have something you can show to potential employers or clients regarding your incorporation of AI into your work.

 
 
Assignment: Final reflection (5% of grade)

What is it?  This will be an in-class handwritten assignment that asks you questions to reflect on what you’ve learned and express your own ideas about this technology.

What is the purpose of it?  The point is to demonstrate your ability to think critically about how you will use this in your own work and life, and for you to assess your own learning.