Course Schedule

*: Lecture notes are intentionally not slides. They summarize key takeaways that emerge verbally during class discussion, hence not on the slides. The listed readings will be covered in class to varying degrees; students are encouraged to read them afterward.

Module 1 - Lay of the Land

Week 1

Course Introduction

• “Do You Have to Pee? A Design Space for Intimate and Somatic Data", Helms, DIS 2019

Week 2

What is AI? [lecture notes][in-class exercise]

Proj1 Task 1 due

• “Artificial Intelligence”, Wikipedia (Chapters 1-3: History, Goals, Approaches)
• “Chinese room”, Wikipedia (the “Chinese room thought experiment” section)
• “Pre-train, Prompt, and Predict" (Chapters 1, 2, and 6), Liu et al., ACM Computing Surveys, 2023

Week 3

Who are the Humans? [lecture notes][in-class exercise]

Proj1 Task 2 due

• “Mapping systems", Stickdorn et al., Chapter 5 of This is Service Design Doing
• “Value tensions in design", Miller et al., GROUP'07

Week 4

What is Human-Centered AI? [lecture notes]

Proj1 Task 3 due

• “Power to the People: The Role of Humans in Interactive Machine Learning", Amershi et al., AI Magazine, 2014
• “Google’s Rules For Designers Working With AI", Schwab, Fast Company, 2017
• “Guidelines for Human-AI Interaction", Amershi et al., CHI 2019
• “Hundreds of AI tools have been built to catch COVID. None of them helped", Heaven, MIT Technology Review, 2021
• “Guide to Automated Journalism" (Introduction & Case studies 1-2), Graefe, Columbia Journalism Review, 2016

Module 2 - Choreographing User-AI Collaboration

Week 5

Introduction to Human-AI Collaboration [lecture notes]

Proj1 Final Submission

• “Literature Review of Teamwork Models (Chapter 4 Human-Agent Teamwork)", Sycara & Sukthankar, 2006
• “The Design of Work Teams", Hackman, 1987

Week 6

🥇 Select Project 1 Share-Out

Division of Responsibilities in Human-AI Teams [lecture notes][in-class exercise]

Proj2 Task 1 due

• “Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes", Yang et al., CHI 2019
• “Designing robots with movement in mind", Hoffman & Ju, Journal of Human-Robot Interaction, 2014

Week 7

AI Explainability, Intelligibility, Transparency [lecture notes]

Proj2 Task 2 due

• “Principles of Mixed-Initiative Interaction", Horvitz, CHI, 1999
• “The Metaphysics of Machine Learning", David Weinberger Google’s People + AI Research Symposium, 2019.

Week 8

AI Trust

Proj2 Task 3 due

• “An Integrative Model of Organizational Trust" (page 4-22, starting from “definition of trust”), Mayer et al., The Academy of Management Review, 1995

Week 9

Project 2 Cook-Off Trial Run

Proj2 Task 4 due

AI Product Development in the Industry

Cross-Disciplinary Collaboration, Capitalism, and More

• “Investigating How Experienced UX Designers Effectively Work with Machine Learning", Yang et al., DIS 2018
• “Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects", Passi & Jackson CSCW 2018
• “A study of UX practitioners roles in designing real-world,enterprise ML systems", Zdanowska & Taylor, CHI 2022
• “Prototyping with Prompts", Subramonyam et al., CHI 2025

Wk 10

Project 2 Cook-Off

The Talk of The Town Human-AI Issues

AI and Creativity, Human Connections, and Job Automation

Wk 11

Happy Spring Break!

Module 3 - Designing AI for Social Good

Wk12

What Could Possibly Go Wrong? [lecture notes]

Proj2 Final Submission

Build a Vocabulary of Unintentional AI Harms

• “Failures of imagination: Discovering and measuring harms in language technologies (video)", Olteanu & Blodgett, 2021
• “Representativeness in Statistics, Politics, and Machine Learning", Chasalow & Levy, FAccT 2021
• “Datasheets for Datasets", Gebru et al., 2018

Wk 13

AI Fairness [lecture notes]

Proj3 Task 1 due

Anticipating and Mitigating AI Harms Falling Unevenly on People

• “Humans are biased too, so why does machine learning bias matter? (8-min video)", Thomas, Video series on Ethics and Machine Learning
• “Algorithmic Fairness: Choices, Assumptions, and Definitions", Mitchell et al., The Annual Review of Statistics and Its Application, 2021

Wk 14

AI in the Wild [lecture notes]

Proj3 Task 2 due

Anticipating and Mitigating Harms Post-Deployment

• “Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design, Yang et al. CHI’20

Wk 15

Project 3 Cook-Off

Course Wrap-Up

Wk 17

Finals Week

Project 3 Final Submission