Course Syllabus

Course Description

This special topic course will delve into the opportunities and challenges associated with generative AI systems. Generative AI systems have been rapidly proliferating and are widely used by laypeople, students, researchers, and developers. While they offer practical human-AI interaction settings, their impact on society remains poorly understood.

Through readings, discussions, scaffolded hands-on technical probing of models, and research-focused projects, students will explore and challenge how to responsibly design, use, develop, and deploy generative AI systems while raising awareness about the numerous open questions that are critically important to the sciences and society. By the end of the course, students will be better positioned to contribute to the discussion and technical advancements needed for responsible and sustainable AI progress. This course is designed for students with diverse backgrounds, ranging from the social sciences to computer and information sciences. Projects will range from literature review to data collection and analysis to user studies to model training or prompting for new tasks evaluation.

Student Learning Outcomes

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

• Identify, articulate, and explain the ethical, sociotechnical, theoretical, and practical opportunities and risks of generative AI.

• Critically assess the value decisions embedded in generative AI systems.
• Work hands-on with generative AI models across modalities (e.g., text, vision) and analyze their behavior on representative tasks.
• Apply methods for evaluating transparency, fairness, and safety in generative AI systems.
• Conduct a full-cycle generative AI project, from scoping and approach selection to interpretation, writing, and presentation.
• Contribute thoughtfully and responsibly to public and scholarly debates on the societal implications of generative AI.

Course Format/Structure

This class blends critical thinking, writing, and speculative design along with hands-on technical experiences; students are required to bring their laptops to class fully charged.

Students can use the following agenda to organize their homework time.

• Deliverables due on Tuesdays are due at midnight.
• Deliverables due on Wednesdays are due at noon.
• In-lecture activities are due by the Friday after the lecture.

Student and Instructor Expectations

We are co-creating this quarter-long learning experience. Making sure the students hit the Intended Learning Outcomes is my priority. Should you have ideas on how to enhance the learning experience for the class, I welcome a conversation and feedback. For class content, whenever you have any questions or anything you'd like to discuss, please come to our office hours.

Communication. If you need to contact the instructor or reader/grader beyond office hours, you can email them directly. We will respond within 48 hours (excluding the weekends). We will notify you in advance if we are traveling for research, and this may cause a delay in our response to Canvas messages or emails. Requirement: Start the subject line of your messages with "IMT 589" so that we do not miss them.

We expect all instructors and students to be respectful in all communication and we’ll hold each other accountable to the iSchool IDEAS values.

Participation. We expect students to participate in discussions by engaging in class activities at least once a week, responding to lecture materials, readings, and in-class examples, sharing timely and relevant new developments, and submitting lecture takeaways.

We expect students to seek out any extra help they need to complete assignments on time, which may sometimes involve learning new skills outside of the assignment (e.g., reading documentation about a technology we use, identifying dataset details, studying the ethical implications of the technology, etc.)

We expect students to contribute meaningfully, collaboratively, and constructively to their group projects.  If you are having trouble contributing to group work, please reach out to your group and, if necessary, the instructors before it becomes a problem. Out of fairness, we will adjust grades if group members report that someone is not participating.

Lecture slides. Slides from the lecture will be posted after class. This is an adaptive class so that the content may change throughout the quarter.

Assignments and Grading

This course is dynamic and evolving. While the weekly highlights may adapt in response to class emerging questions, the expectations for assignments remain consistent: thoughtful completion, deep application of course concepts, critical reflection, and meaningful contributions to the broader conversation on ethics in generative AI. Students are encouraged to stay curious, ask questions, and engage actively with both the material and each other.

Grading Breakdown

Students will be assessed through the following components:

• Lecture debriefs (10%): After each lecture (8 regular sessions + 2 final project presentations), students will complete a debrief on Canvas by the end of the day.
• Homework (30%): There are 5 assignments, each worth 6%, designed to build conceptual and practical skills over time.
• Practical Midterm (20%): A creative, in-person, hands-on project. (See "Practical Midterm” section for full details.)
• Final Project (40%): A self-directed project to contribute to an area of interest in depth. (See “Final Project” section.)

Grading Scheme

This course follows the  iSchool Grading Scales, which converts percentage grades to a 4.0 scale.

Extra Credit

Extra credit opportunities (up to 3%) may be offered during the term and will be announced when available.

Late Work

• Students have two free late days to use across any of the five homeworks.
• Beyond that, 10% per day will be deducted from the assignment grade.
• If unexpected circumstances arise, students must request an extension at least one week in advance to opt for full credit; otherwise these will not be accepted.

Practical Midterm

Before diving deep into their Final Projects, students will have a chance to pause, reflect, and showcase what they’ve learned so far—through an individual, creative, hands-on midterm. This open-book (but close-laptop), in-class activity is a remix of Homework 1, with a few unexpected twists, and will draw from materials explored in homeworks, lectures, and student-curated sources. Students will be prompted to map out tensions and opportunities within these resources, applying course concepts in thoughtful and imaginative ways. No cramming required—just curiosity, engagement, and a willingness to cut, paste, and connect the dots. A great way to prepare is by iteratively building on the conceptual map from Homework 1 as the course progresses.

Final Project

Over the course of the quarter, students will work in teams of 3 to explore a generative AI ethics topic that sparks both shared interest and productive tension. The challenge? To connect your unique background––whether as a builder, thinker, citizen, or critic––with the concepts from class, and create something original that contributes to the evolving conversation on Ethics in Gen AI.

This is not just a research paper. It’s a report on something real your team has created: a prototype, game, dataset, user study, toolkit, policy draft, explainer, visualization, or speculative design––anything that meaningfully applies course ideas to real-world or speculative scenarios.

The project will unfold in milestones and include peer feedback checkpoints. Final evaluation will consider not only the originality and relevance of your idea, but also your team’s ability to articulate a clear question, justify your design choices, evaluate your artifact (qualitatively or quantitatively), and reflect on its implications. The project culminates in an oral presentation and peer-reviewed research-style report.

You don’t need to solve generative AI. But you do need to ask a thoughtful question, explore it with care, build something that others can learn from, and show what you've discovered along the way. Think of it as your team’s opportunity to say: “Here’s a gap we see. Here’s how we begin to bridge it. And here’s what we’ve learned in the process.”

Course Material

Students will find recommended course material in the specific slide deck for each lecture. In addition to those, core resources are: 

• O'neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
• Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
• Buolamwini, J. (2023). Unmasking AI: My Mission to Protect What Is Human in a World of Machines. Random House. (336 pp). ISBN-13: 978-0593241837
• Hao, K. (2025). Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI. Penguin Press. (496 pp). ISBN-13: 978-0593657508
• Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM FAccT.
• Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., ... & Rahwan, I. (2018). The moral machine experiment. Nature, 563(7729), 59-64.

Collaborating with Generative AI

Co-shaping the learning experience

During the first two weeks of the course, students and the instructor will engage in a values-based conversation to co-develop shared expectations around student work and instructor feedback in relation to generative AI tools. The outcome of this agreement will be published on Canvas and will guide the extent to which the use of generative AI in assessments is encouraged, discouraged, or prohibited.

Prior to that agreement, the following default guidelines apply:

• Lecture Debriefs: Do not use generative AI. These are meant to reflect your own thinking and learning in your own words. No need to worry about grammar––this is a space for honest reflection, not polished writing.
• Homeworks: Expectations will vary. Each assignment will specify its policy.
• Practical Midterm: In-person, closed-laptop.
• Final Project: Each deliverable will include specific guidance on permitted tools and uses.

In situations not covered by the class agreement, the default policy is (adapted from Ack [1]):

1. do not use Generative AI to do your work for you. Using generative AI very carefully might be acceptable after you try your best to think critically, study, write, solve problems, code, answer questions, and debug.
2. report in your work any significant use of sophisticated tools, such as instruments and software; we now include in particular text-to-text or multimodal generative AI among those that should be reported consistent with subject standards for methodology.
3. remind you that each of you individually take full responsibility for all the submission contents, irrespective of how the contents were generated. If generative AI language tools generate inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in your work, it is your responsibility.
4. generative AI language tools should not be listed as an author; instead authors should refer to (2).

Academic Conduct

Please review the iSchool Academic Policies.

The University takes academic integrity very seriously. Behaving with integrity is part of our responsibility to our shared learning community. If you’re uncertain about if something is academic misconduct, ask me. I am willing to discuss questions you might have.

Acts of academic misconduct may include but are not limited to:

• Cheating (working collaboratively on homeworks/midterm and discussion submissions when collaboration is not allowed, and previewing quizzes/exams)
• Plagiarism (representing the work of others–human or AI–as your own without giving appropriate credit to the original author(s))
• Any Unauthorized collaboration

Concerns about these or other behaviors prohibited by the Student Conduct Code will be referred for investigation and adjudication by (include information for specific campus offices). Students found to have engaged in academic misconduct may receive a zero on the assignment (or other possible outcome).

IDEAS

From the IDEAS section of the iSchool website: We create an environment that fosters appreciation, mutual respect, and engagement among and between members of the iSchool, UW community, and beyond, with special attention to the needs of people from historically marginalized communities. We envision a university in which all students, faculty and staff participate fully and meaningfully in campus life without being subjected to discrimination, bias or microaggressions. We condemn any expressions of racism, sexism, homophobia, transphobia, ableism, or any other instances of bias and discrimination against marginalized individuals or groups.

Wellbeing and Support Resources

Your wellbeing is important. There may be times when stress, life challenges, or difficult emotions affect your academic experience and daily life. If this happens, please know you are not alone, and support is available.​

The University of Washington Counseling Center (mentalhealth.uw.edu | 206-543-1240) offers free and confidential services during business hours to support you with stress, adjustment, or mental health concerns. For 24/7 support, you can call the Husky Helpline at 206-616-7777 or the National Suicide & Crisis Lifeline by dialing 9-8-8.​ For students enrolled in an Information School program, you may reach out to book a confidential appointment with the Mental Health Counselor, Leigh Eisele, at leisele1@uw.edu.

Reaching out for support is a sign of strength. Whether you're seeking tools to manage stress, someone to talk with, or resources for a friend, help is here.

Excused Absence from Class

Students are expected to attend class and to participate in all graded activities, including midterms and final examinations. A student who is anticipating being absent from class due to a Religious Accommodation activity needs to complete the Religious Accommodations request process by the second Friday of the quarter. Students who anticipate missing class due to attendance at academic conferences or field trips, or participation in university-sponsored activities should provide a written notice to the instructor with two weeks of notice ahead of the absence. The instructor will determine if the graded activity or exam can be rescheduled or if there is equivalent work that can be done as an equivalent, as determined by the instructor.

Religious Accommodations

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Acknowledgments

This course is built on the learnings from my journey as a researcher and educator in Human-Centered NLP and AI Systems; the syllabus and resources are heavily inspired on the work of current leaders in our area, with specific emphasis on the following courses:

[1] University of Washington, IMT 589: Generative AI Ethics. Aylin Caliskan. Autumn 2024

[2] MIT, 6.S062/MAS.S10 Gen AI in K12 Education. MIT Csail x MIT Media Lab. Fall 2023.

[3] Columbia, IKNS 5992: AI and the Knowledge Driven Organization. Nicole M. Alexander. Fall 2025

[4] MIT, MAS630: Affective Computing and Ethics. Rosalind Picard. Fall 2023 Fall 2024

[5] Penn State, IST 597: Community AI Development and Evaluation. Dana Calacci. Fall 2025

[6] Thanks also to Applied Ethics in AI researchers Daniella DiPaola and Elinor Poole-Dayan.