Human-Computer Interaction and AI

What Practitioners Need to Know to Design and Build Effective AI Systems from a Human Perspective

A course at CHI 2024

Everything you need to know about the course to be taught at CHI 2024 in Honolulu, Hawai'i (USA)

Welcome to our course site for the HCI + AI course at CHI 2024 in Hawai'i!

As you know, AI and ML are now essential parts of many systems that are currently being built-they're everywhere!

What should CHI practitioners know about the possibilities and potential drawbacks of building AI systems?

Understanding the human side of AI/ML based systems requires understanding both how the system-side AI works, but also how people think about, understand, and use AI tools and systems.

This course will cover what AI components and systems currently exist, how to design and build usable systems with AI components, along with how the mental models of AI/ML tools operate. These models lead to user expectations of how AI systems function, and ultimately, to design guidelines that avoid disappointing end-users by accidentally creating unintelligible AI tools. We'll also cover the ethics of AI, including data collection, algorithmic and data fairness considerations, along with other risks of AI.

What you need to know about the class: The course will be given wholly in-person on May 15, 9AM - 5PM.

Both classes will be held in room 323A at the Hawai'i Convention Center. Please see this map for exact location. Note that it's in an out-of-the-way location. You might try finding it before the course date, just so you'll know where it is.

We'll be spending our time doing a number of different labs to give you some rapid-fire, hands-on experiences.

Since this will be primarily a hands-on course, plan on coming to the course with a laptop that you've successfully connected to the conference hall network. Bring a full battery or be willing to share power cords with friends.

Hari Subramonyam

I am a Research Assistant Professor at the Graduate School of Education and Computer Science (by courtesy) at Stanford University. I am also the Ram and Vijay Shriram Faculty Fellow at the Institute for Human-Centered AI (HAI) and a core faculty member of Stanford HCI. My research sits at the intersection of Human-Computer Interaction (HCI) and the Learning Sciences. I study ways to augment human learning using AI by (1) engaging in cognitively informed design practices, (2) co-designing with learners and educators, and (3) developing transformative AI-enabled learning experiences. Through my research, I also contribute tools and methodologies that prioritize ethical considerations, responsible design practices, and human values when creating AI experiences. I received my PhD in Information at the University of Michigan, advised by Eytan Adar.

Daniel M. Russell

I am a traveling scholar. As such I write, lecture, and create materials for teaching. Sometimes this includes videos, short podcasts, tech reports, sometimes papers for scientific publication, and sometimes books for everyone.

I'm a practicing scientist. That means I experiment and I analyze. I do field studies and I try to understand what makes online researchers tick.

Why do they sometimes query Google for [ first ], and then not click on anything?

Why do some Google users only ask one query, while others can go on and on?

What's different about their search experiences?

Why? This is what drives my work: What do people search for? How do they do it? How do they understand and use what they've found.

Chinmay Kulkarni

My research introduces technological means to help people help each other by sharing knowledge, drawing on shared experiences, and supporting each other. In doing so, I hope to make sustainable progress on long-standing problems in learning and work.

Recent questions in my research include: How might teachers help each other with teaching techniques adapted to the pandemic? How might entrepreneurs help each other with feedback and serve overlooked needs? How might remote workers develop more sustainable careers? How might knowledge workers make better decisions through improved perspective-taking? How might voice interfaces inspire collaboration?

Elena Glassman

I design, build and evaluate systems for comprehending and interacting with population-level structure and trends in large code and data corpora. I am an Assistant Professor of Computer Science at the Harvard Paulson School of Engineering & Applied Sciences and the Stanley A. Marks & William H. Marks Professor at the Radcliffe Institute for Advanced Study, specializing in human-computer interaction.

Nikolas Martelaro

I am an Assistant Professor at Carnegie Mellon's Human-Computer Interaction Institute. My lab focuses on augmenting designer's capabilities through the use of new technology and design methods. My interest in developing new ways to support designers stems from my interest in creating interactive and intelligent products. I blend a background in product design methods, interaction design, human-robot interaction, and mechatronic engineering to build tools and methods that allow designers to understand people better and to create more human-centered products. Before moving to the HCII, I was a Digital Experiences researcher at the Accenture Technology Labs in San Francisco. I graduated with my Ph.D. in Mechanical Engineering from Stanford's Center for Design Research, where I was co-advised by Larry Leifer and Wendy Ju.

5 Instructors, 5 Topics in HAI
In the sections below you'll find each instructor's videos on their topic.

Hari Subramonya on collaborating with / using AI systems

Nikolas Martelaro on ideating in HAI with prototyping
(PDF)

Chinmay Kulkarni on HCI aspects of LLMs
(PDF) Chinmay lecture slides (Doc for Chinmay's prompt )

Dan Russell on HAI aspects of art, creativity, and synthesis
(PDF)

Elena Glassman on an HAI model of interaction as conversation (PDF)

Panel discussion on HCI & AI (April 11, 2023)

Plan for the course

Course plan:

Our plan for the course is to cover several areas in four 75 minute sections in a mixture of lecture with hands-on exercise work. We'll be covering:

* The human aspects of designing and building AI/ML systems - practice and theory

- How people understand AI systems: AI and mental models

- examples/cases of AI system UX

* Designing for AI failures and Feedback to Users

- guardrails and failure modes

- details on what has worked, what hasn’t worked, and why

* Data, Knowledge, Fairness, and Ethics

- AI Ethics of Actions, Fairness, Social Acceptability, and Trust

- analysis methods to understand HAI data with respect to fairness

* Interpreting and Explaining AI Algorithms and Systems

- Building AI/ML with humans in the loop / AI in the loop

- understanding spoken language; written language;

- generating language; conversations; large language models such as ChatGPT, Llama, or Bard

* Computer perception: recognition, classification, uses (and misuses)

* AI & Art: Synthesis systems for creativity, music, imagery

- prompt engineering (what it is and how to do it)

* Where does the future of AI/ML and HCI lead?

- HAI: how to design and build real systems

- Or is AI/ML in an existential crisis? If so, how and why? What can we do about it?

The course will include several in-class exercises with existing AI tools. Materials for the exercises will be made available in the class via a public class website. Participants should plan on bringing a laptop to the class (or be willing to partner with someone who has one).

Agenda for the Day: