AI Law and Policy Class Syllabus
You've asked, so here it is.
For those of you who “live by the syllabus,” here’s the class fully laid out, with the hyperlinks to each class. Please make proper attribution to these materials if you use them to teach with or share these materials.
AI Law and Policy Fall 2025 Syllabus
Class #1 (August 25): AI Fundamentals (What is AI)
Class #2 (August 27): AI Fundamentals (How Actually Works)
Class #3 (Sept. 3): Open Source Debate (Open v. Closed Models: What Happens When the World Runs on Chinese Code)
Class #4 (Sept. 8): Open Source Debate (The AI Control Paradox, Open Source Models)
Class #5 (Sept. 10): AI Model Training, Outputs, and Copyright (The $1.5 Billion Question: How AI Learns from the World’s Words)
Class #6 (Sept. 15): AI Model Training, Outputs, and Copyright (Training Data, Discovery Wars, and Who Gets Paid: When Courts Meet Code)
Class #7 (Sept. 17): Compute Power in AI (Why China Quit US Chips: When Controlling Compute failed to control AI)
Class #8 (September 22): Compute Power and Governance (Your Electricity Bill Is About to Become an AI Policy Issue: The Compute Governance Paradox)
Class #9 (September 24): Bias and Fairness (When AI Discrimination Happens 1.1 Billion Times: The Scale of Algorithmic Bias)
Class #10 (September 29): Transparency (The Glass Box Paradox, When AI Can’t Explain Itself: When Seeing Isn’t Understanding)
Class #11 (October 1): Accountability and Liability (When AI Fails, Who Pays?: The Bromide Poisoning and the Liability Puzzle)
Class #12 (October 6): AI Safety, Robustness, and Adversarial Attacks (Red-Teaming AI When AI Red-Teams You: “I think you’re testing me,” says Claude)
Class #13 (October 8): AI Safety, Robustness, and Adversarial Attacks (Red-Teaming Governance: From Practice to Law: When the NYT Became a “Hacker”)
Class #14 (October 20): Content Authentications, Deepfakes, Misinformation (When Anyone Can Fake Anything: Deepfake Technology and the Post-Truth World)
Class #15 (October 22): Content Authentications, Deepfakes, Misinformation (Why Governing AI Synthetic Media is So Hard—And Why Everyone’s Trying To Anyway)
Class #16 (October 27): Manipulation and Deception in AI Models (When AI Learns to Manipulate: The Line Between Harm and Exploitation)
Class #17 (October 29): Manipulation and Deception in AI Models (Governing AI Manipulation Through Five Paradigms).
Class #18 (Nov. 3): Data privacy (Data Privacy in an AI World).
Class #19 (Nov. 5): Equity, Civil Rights, Human Rights (When Invisible Algorithms Judge You)
Class #20 (Nov. 10): Risk and AI Governance (EU Approach) (The EU AI Act’s Reality Check)
Class #21 (Nov. 12): Risk and AI Governance (EU Approach) (Understanding How the EU Regulates AI)
Class #22 (Nov. 17): US Approach (Geopolitical and National Security Issues) (U.S. AI Policy Myths and Realities)
Class #23 (Nov. 19): US Approach (Geopolitical and National Security Issues) (When Silicon Valley’s Effective Altruists Meets Washington’s Export Controls)
Class #24 (Nov. 24/Dec. 1): Special Guests, Randall Cook and Casey Mock (Special Edition Coming December 15)
Class #25 (Dec. 1): China and AI Regulation (Are Different Players Running Different Races?)
Class #26 (Dec. 3): AI Agents, and Looking Ahead (When AI Stops Advising and Starts Acting)
Nita, this syllabus captures how law is finally beginning to mirror the structure of the technology it seeks to regulate. The sequencing, from model training to accountability, reflects the lifecycle of AI itself.
I recently published a paper titled “Linguistic Homogenization in AI-Generated Content: Cultural Impacts and Implications for AI Safety and Control,” exploring how repetitive rhetorical patterns in LLM outputs influence public reasoning and discourse diversity. It seems increasingly relevant to the “bias and fairness” and “manipulation and deception” topics in your course. The legal frameworks you’re teaching could one day determine how we classify and mitigate these subtle linguistic risks, where persuasion becomes a form of governance itself.