Brown University · Department of Computer Science · Fall 2026

CSCI 1953CLegal Issues in Generative AI

Instructor
Jeff Huang
Class
Fridays 3–5:30pm in 477 CIT

About the Course

Generative AI has raised new questions of law, from the data collection, to the model training and output, to the harms that can arise. Courts apply existing legal doctrines to these technologies, yet how those laws govern generative AI remains unsettled. This seminar will cover the legal procedures, and analysis of Generative AI products. The focus will mostly be on judicial precedent in property, contract, and tort doctrines and how they manifest during the AI lifecycle: from data collection, to model training, to inference, to generative harms. Additionally, the seminar will examine some recent state and international regulations.

The goal will be to develop the skills to analyze opinions, and assess how courts may resolve ongoing and future disputes involving generative AI, both in traditional doctrines and more recent regulations. What is most exciting about legal issues in Generative AI, is that much of the law is not yet settled, and will be revealed through the next decade of ongoing high-stakes litigation.

The seminar will feature a mix of short lectures reviewing both technical and legal concepts, followed by discussion of readings. The readings will include both foundational case law, as well as current litigation at various stages. Learning activities outside of class will include 4-5 readings per week, a mid-semester assignment, along with using LexFactor for a guided discussion of the reading. There will be a final exam covering all the topics learned during the semester.

Note that AI or tech policy is not an objective of this course, as there are other courses at Brown that cover policy more comprehensively. I encourage you to take those courses if you are interested in making changes to AI or tech governance and regulations. Additionally, this course focuses more on the new legal issues that arise from generative AI, and less about issues from AI such as bias/discrimination/fairness and privacy, each of which are better represented in other courses at Brown that focus more on those topics specifically.

Learning Objectives

By the end of the course, students will be able to:

Schedule

Note: readings are subject to change as the seminar progresses. Please do not read ahead more than 1 week.

Week 1
Sep 11

Intro

CS
Overview of the Generative AI lifecycle
Law
How to read a court opinion / pleadings and procedures in a lawsuit
Week 2
Sep 18

Acquiring data through scraping

CS
Scraping the web, authorization, robots.txt
Law
Terms of service, contracts of adhesion, breaches, CFAA
Cases
hiQ v. LinkedIn (two court outcomes), Meta v. Bright Data, Register.com v. Verio, Reddit v. Anthropic
Week 3
Sep 25

Handling licenses attached to data

CS
Open source, collections, open-weight model licenses
Law
Copyright basics, licenses, first sale doctrine
Cases
Feist v. Rural, Jacobsen v. Katzer, Bartz v. Anthropic (but skip the fair use part for now), Doe v. GitHub
Week 4
Oct 2

Copying within the data pipeline

CS
hardware/software data pipeline: memory, disk, copies
Law
Fixation, intermediate copying, memorization
Cases
Sega v. Accolade, Cartoon Network v. CSC Holdings, MAI Systems v. Peak, Andersen v. Stability AI
Week 5
Oct 9

Training the model and fair use

CS
Representation: Tokenization and embeddings
Law
Fair use factors, what is transformative
Cases
Authors Guild v. Google, Warhol v. Goldsmith, Thomson Reuters v. Ross Intelligence, Google v. Oracle, Kadrey v. Meta Platforms, revisit Bartz for fair use
Week 6
Oct 16

Violations in the model output

CS
Style transfer, deepfakes
Law
Substantial similarity, trademarks, right of publicity, derivative works
Cases
Jack Daniel's v. VIP Products, Midler v. Ford, Lehrman v. Lovo, New York Times Co. v. OpenAI, Disney v. Midjourney, Cox v. Sony
Week 7
Oct 23

Privacy and active training

CS
Post-training, RLHF, distillation, and human review
Law
Privacy law, trade secrets, and confidentiality
Cases
ACLU v. Clearview AI, USA v. Heppner, re Google Assistant Privacy Litigation, xAI Corp v. OpenAI, Brewer v. Otter.ai
Week 8
Oct 30

Search and attribution

CS
Retrieval-augmented generation and web search
Law
Display and attribution, substitution, agency law
Cases
Perfect 10 v. Amazon, Dow Jones v. Perplexity AI, Associated Press v. Meltwater, Amazon v. Perplexity AI, Watteau v. Fenwick
Week 9
Nov 6

Authoring, and what it means

CS
Prompting, predictability
Law
Creativity, human vs AI authorship, inventorship
Cases
Burrow-Giles v. Sarony, Thaler v. Perlmutter, Zarya of the Dawn, Allen v. Perlmutter, Thaler v. Vidal
Week 10
Nov 13

Responsibility for generated content

CS
Hallucinations, context, drift
Law
Section 230, defamation, free expression
Cases
Walters v. OpenAI, Zeran v. America Online, Mata v. Avianca, Moody v. NetChoice, Moffatt v. Air Canada
Week 11
Nov 20

Torts for serious harms

CS
Alignment, guardrails, and jailbreaking, chatbots
Law
Product or service?, Tort framework: negligence, duty, causation, damages, Product liability law
Cases
Raine v. OpenAI, Garcia v. Character Technologies, Lemmon v. Snap, Greenman v. Yuba Power Products, James v. Meow Media
Week 12

Thanksgiving

Week 13
Dec 4

Regulations

CS
Watermarking and auditing
Law
State and EU statutes/regulations
Cases
Mobley v. Workday, EU AI Act, Colorado AI Act, California statutes, OpenAI v. Garante

Learning Activities and Assessments

Leading Discussion10%
Discussion Notes 1% each10%
Discussion Reflections 2% each16%
Assignment24%
LexFactor 1.5% each15%
Final exam 1% per question25%

Letter grades are calculated automatically at the end of the semester. The thresholds for A/B/C cutoffs are 90/80/70. Historically, the distribution of grades are roughly in line with the distribution in our department or division, see the Brown Courses Factbook for the breakdown, but neither the course nor the assignments are graded on a predetermined distribution (i.e., nothing is graded on a curve).

Leading Discussion

Students will choose a week in which they will lead the discussion during class. They should prepare notes for how the discussion should go, including a very short summary of the readings, prompts (open-ended questions) to the class, any timely information that is interesting to share, and a plan for the flow of the discussion.

Discussion Notes

Students bring paper notes to class, outlining cases in preparation for discussion. See the FAQ for more details.

Discussion Reflection

Students will submit reflections for their discussion contributions (i.e., their participation in the class discussion) in 8 classes, no more than 100 words. The reflections should describe what was being discussed before their contribution, what they contributed and why, and how their contribution related to the topic in class or the reading. Reflections should be written by the student without the assistance of AI tools.

Assignment

An assignment to be released during the mid-semester, due before Thanksgiving break, to provide a legal analysis on a technical scenario that is reported in current events.

LexFactor

Readings will be available in LexFactor, with the relevant parts excerpted. LexFactor sessions are open book and there is no time limit, but do not permit the use of AI tools. Assessment is based solely on the student's own contributions during the conversation. Like a graded reading comment, credit reflects what the student personally writes about the key issues in the reading, while the exercise also functions as an assessment like a quiz with guided questions. The complete transcript of each session is shared with the course staff, who finalize a grade based on what key issues are covered by the student. Each session is expected to take approximately one hour, and students should complete the assigned reading before beginning. LexFactor can be accessed on https://lexfactor.cs.brown.edu/

Examination

The final examination will be held at the time designated for this course during the end-of-semester examination period. It will be an in-person closed-book multiple-choice exam.

FAQ

How do I get into the course?

There are no prerequisites whatsoever for this course, any student enrolled at Brown may request to be enrolled and will be considered.

An override request form will be made available on the course website following the first class meeting on Friday and must be submitted by the end of the day of the following Monday. Override codes will be distributed by the following Wednesday evening to 20 students, prior to the second class on Friday. No additional override codes will be distributed after Wednesday. Students do not need to submit an override request through CAB at this time, nor contact the instructor by email.

Can I use generative AI in this course?

Unless an assignment or activity explicitly states otherwise, you may not use generative AI tools in any capacity to complete course assignments, including during discussions and on LexFactor.

In the context of this course, “generative AI tools” include agents such as ChatGPT, Claude, Gemini, Copilot, Perplexity, Mistral, and other systems that generate or transform text, ideas, code, images, and other content.

Submitting AI-generated or AI-assisted work as your own may constitute a violation of the University's academic integrity policies. Therefore, the prohibition on AI use applies even if you have substantially edited, rewritten, or cited AI-generated material, as the goal of this policy is to ensure that the work you submit reflects your own thought processes and ideas. If you have questions about what tools are allowed on assignments, please ask the instructor as soon as the assignment has been introduced.

Can I bring my laptop to class?

In this class, we encourage active discussion, close reading, and independent engagement with course materials. Electronic devices, including laptops, iPads/tablets, and phones, can distract from learning in the immediate environment, and studies have shown that they reduce retention. Therefore, in this class, we will use a largely paper-based notetaking and discussion format that is designed to support your attention to one another and the course material.

Electronic devices should be put away during class except where they are needed for an approved accommodation or a specific class activity that uses them. This course also values good notetaking (i.e., briefs) for outlining the key points in a court case. Please bring your notes to class in print, on paper, and use those paper notes you have prepared before class.

What notes do I prepare before class, and how do I earn participation points?

For each assigned reading, you should prepare a brief, comprising handwritten notes on paper outlining the key issues of the cases before class demonstrating that they have actively understood and analyzed the material. Include much of the following:

  • the legal question under dispute
  • the key facts of the case
  • procedural history
  • the relevant law(s), how those laws apply to the facts
  • court holding (decision)
  • any of your thoughts or questions about the case

Your notes do not need to be clean or long; the purpose of this exercise is to give you more information to bring into discussion and encourage you to engage with the material in your own words. As such, your notes should reflect only your thoughts and ideas; do not submit AI-generated notes in place of your own.

You may earn 1 point for participation in each class by submitting a clean photograph/scan of your notes to Canvas before class. Please clearly identify the case material you have chosen to take notes on. These points are meant to reward consistent preparation, demonstrating that you have taken effort to think critically about the material before coming to class. If you have an approved accommodation that makes paper-based participation difficult, please speak with the instructor to identify an alternative.

What happens if I miss class?

A substantial portion of the learning in this seminar takes place during class meetings, including in-class discussions. Students should participate in class by contributing to the discussion with thoughtful comments, ideally focused on the readings and the topics presented in class. Students who miss more than two classes should expect their grade to be substantially affected, as there are limited opportunities to make up missed work.

How much work is this course?

The course is expected to take 180 hours of work during the semester, comprised of weekly class, discussion reflections, readings, an assignment, LexFactor sessions, and preparation for the exam.

What if I submit something late?

Discussion reflections, assignments, and LexFactor sessions submitted late will take 1% off from the course grade for each day late. For example, a LexFactor session submitted 1 day late will be able to receive at most 1 of 4 points.

Will there be sensitive content?

Due to the nature of the course, students will encounter content about harm and other sensitive topics. Some court cases may describe events about suicide, abuse, death, in classes about chatbot liability and harm.

How do I request an accommodation?

Brown University is committed to full inclusion of all students. Please inform me early in the term if you may require accommodations or modification of any of course procedures. You may speak with me after class, during office hours, or by appointment.

Student Accessibility Services (SAS) provides accommodations and support for undergraduate and graduate students with disabilities. To learn more or to request disability accommodations, reach out to SAS at sas@brown.edu, 401-863-9588, or visit the SAS website. If you encounter any digital materials in this course that are inaccessible, complete the Digital Accessibility Concern Reporting Form. For any accessibility or accommodation concerns, please contact the ADA/504 Coordinator at ada_504@brown.edu.

Undergraduates in need of short-term academic advice or support can contact an academic dean in the College by emailing college@brown.edu. Graduate students may contact one of the deans in the Graduate School by emailing graduate_school@brown.edu.

Can I take an Incomplete?

A grade of Incomplete will be considered only when a single requirement remains outstanding at the end of the semester, and only with a Dean's Note specifically recommending an Incomplete for good cause, together with the approval of the instructor. If that outstanding requirement is the final examination, it may be completed only in the following semester by sitting the examination for CSCI 1400, and such an arrangement is not guaranteed.