SYS 478: Fall 2026

Course Schedule

Course Schedule

A semester overview of the course topics, meeting dates, readings, and assignments.

  1. Tue, Aug 18
    1.1 Why Does AI Matter as a Public and Ethical Issue?Framing AI as a Public, Ethical, and Contested Question
  2. Thu, Aug 20
    1.2 How Do Humans Learn? Biological FoundationsBrains, neurons, networks, and plasticity as a foundation for thinking about learning before we turn to machines.
    Readings (due before class)
    • Saplakoglu, Y. (2026, April 24). A new type of neuroplasticity rewires the brain after a single experience. Quanta Magazine. Link
    • Vignola, N. (2024). A neuroscientist's guide to reclaiming your brain [Video]. Big Think. Link
    • Sentis. (2012). Neuroplasticity [Video]. YouTube. Link
    DueInitial Thoughts on AI3:00 PM
  3. Tue, Aug 25
    1.3 How Do Humans Learn? Psychological FoundationsIndividual Learning: Behaviorism and Constructivism
    Readings (due before class)
    • Sawyer, R. K. (2014). Introduction: The new science of learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 1–18). Cambridge University Press. Link
      Available via the Ramsey Library. Please log in via your UNCA Account
    • Pick one
      • Wikipedia contributors. (n.d.). Jean Piaget. In Wikipedia. Link
      • Wikipedia contributors. (n.d.). B. F. Skinner. In Wikipedia. Link
    DueBiological Intelligence Reflection3:00 PM
  4. Thu, Aug 27
    1.4 How Do Humans Learn? Sociocultural FoundationsSocial and Political Learning: Vygotsky and Freire
    Readings (due before class)
    • Teachers College, Columbia University. (n.d.). Convocation Masters III: Medalist Luis C. Moll [Video]. YouTube. Link
      (start at around minute 6).
    • Pick one
      • National Academies of Sciences, Engineering, and Medicine. (2018). Context and culture. In How people learn II: Learners, contexts, and cultures. The National Academies Press. Link
      • Freire, P. (1970). Chapter 2. In Pedagogy of the oppressed. Continuum. Link
  5. Tue, Sep 1
    1.5 Judgment & EthicsA shared case and seven moral lenses: what human judgment adds beyond fluent performance.
    Readings (due before class)
    • Markkula Center for Applied Ethics. (n.d.). A framework for ethical decision making. Santa Clara University. Link
  6. Thu, Sep 3
    1.6 From Human Learning & Judgement to AI SystemsWhat do we need to understand about how an AI system works before we can evaluate it well?
    DueCareer Module 111:59 PM
  1. Tue, Sep 8
    2.1 How Does the World Become Data?Computers never encounter the world directly. They encounter encoded representations of it.
  2. Thu, Sep 10
    2.2 How Does a Computer Actually Work?A conventional computer takes represented information and follows stored instructions to transform it into an output.
    Readings (due before class)
    • Newsthink. (n.d.). How the modern computer was invented by accident [Video]. YouTube. Link
    DueHW 111:59 PM
  3. Tue, Sep 15
    2.3 From Rules to Examples: What Changes When a System Learns?Hand-coded rules and learning from labeled examples place human choices in different parts of a system.
    Readings (due before class)
    • Abu-Mostafa, Y. S. (2012, July). How to teach computers to learn on their own. Scientific American, 307(1), 78–81. Link
    • History of AI timeline (Course website) Link
  4. Thu, Sep 17
    2.4 Learning From Data: Training Sets, Errors, and BiasTraining examples shape what a model learns; the consequences of errors depend on whose examples and needs are represented.
    Readings (due before class)
    • Intro to Supervised Learning (Course Website) Link
    • Zewe, Adam. “Can Machine-Learning Models Overcome Biased Datasets?” MIT CSAIL, 2 Mar. 2022. Link
  5. Tue, Sep 22
    2.5 Inside a Neural Network: Weights, Layers, and LearningWeights and layers transform features into predictions; training adjusts weights, while people choose the task and evaluation criteria.
    Readings (due before class)
    • Crawford, Kate. “Atlas of AI.” Sydney Ideas, University of Sydney, 2021. Link
      Ideally, watch the whole thing. If you're short on time, watch minutes 11-24. Discusses training data, ImageNet, classification, and how human categories shape what AI systems learn to recognize.
    • Sanderson, Grant. “But What Is a Neural Network?” 3Blue1Brown, 5 Oct. 2017. Link
      Watch approximately the first 8–9 minutes for an intuitive introduction to neurons, layers, and learned features.
    DueCareer Module 211:59 PM
  6. Thu, Sep 24
    2.6 Making Up People: Categories, Classification, and ClusteringClustering groups examples without supplied group labels; features and similarity measures shape the resulting categories.
    Readings (due before class)
    • Thorn, A. (2021). Social Constructs [Video]. Philosophy Tube. Link
    • J, Sara. “Why Clustering Algorithms Work So Well in Music Apps.” Medium, 16 Sept. 2026. Link
      Introduces clustering and k-means through music recommendation and playlist examples.
  7. Tue, Sep 29
    2.7 Language Models: How can predicting the next word produce something so useful?
    Readings (due before class)
    • Doctorow, C. (2026, September 12). LLMs are real, AI is fake. Pluralistic. Link
    • 3Blue1Brown. (2024). Large Language Models explained briefly [Video]. Link
      Tokens, training, and generation; connect outputs to evidence.
    DueTeachable Machine (Supervised Learning) Reflection11:59 PM
  1. Thu, Oct 1
    3.1 Class SYS Brainstorm
    DueGroup Brainstorm: Your Vision for 204611:59 PM
  2. Tue, Oct 6
    Fall BreakNo class
  3. Thu, Oct 8
    3.3 Identify shared themes and messaging, form topic teams, and assign responsibilities
  1. Tue, Oct 13
    4.1 Do technologies have politics?
  2. Thu, Oct 15
    4.2 AI for What? Progress, Benevolence, and SolutionismStudent-selected investigation
  3. Tue, Oct 20
    4.3 Fake News and Trustworthy ContentStudent-selected investigation
  4. Thu, Oct 22
    4.4 Creativity, Training Data, and CopyrightStudent-selected investigation
  5. Tue, Oct 27
    4.5 Technology & Business ModelsStudent-selected investigation
  6. Thu, Oct 29
    4.6 Data Centers and Resource UseStudent-selected investigation
  7. Tue, Nov 3
    4.7 The Future of WorkStudent-selected investigation
  1. Thu, Nov 5
    5.1 Explainability: Whose Questions and Knowledge Count?System mechanisms limit what an explanation can establish; audience needs and experiential knowledge shape what must be explained.
  2. Tue, Nov 10
    5.2 Auditability, Contestability, and Post-Deployment ControlAdaptation changes behavior; logging, access, and permission designs determine whether responsible institutions can investigate and intervene.
  3. Thu, Nov 12
    5.3 Who Should Shape Emerging Technology?Design specifications encode priorities; public participation can change purposes and controls when consequential choices remain open.
  4. Tue, Nov 17
    5.4 Fall SymposiumDetails TBD!
  5. Thu, Nov 19
    5.5 Public-Engagement PilotHow mechanisms are explained shapes deliberation; participants’ questions can expose missing evidence, alternatives, and design requirements.
  6. Tue, Nov 24
    5.6 What Did Engagement Change?Public concerns may require new evidence, different controls, revised purposes, or non-adoption; technical feasibility constrains proposed responses.
  1. Thu, Nov 26
    Thanksgiving BreakNo class
  2. Tue, Dec 1
    6.2 Professional and Civic ResponsibilityTechnical choices and institutional responsibilities shape one another; credible public communication makes both visible.
  3. Final exam period
    6.3 Public Explainers and ELSI PresentationsExplain a chosen AI system and defend its purposes, evidence, alternatives, and governance.