Syllabus

What is HCI?

Human computer interaction (HCI) is a broad, interdisciplinary field that studies how people and computers interact in the world. These interactions can be studied at the individual scale (e.g. how intuitive an app or website is to a particular user) or in relation to larger social / cultural phenomena (e.g. how social media influences the way people shop, communicate, or access information).

HCI draws on knowledge and expertise from many different disciplines and domains (e.g. computer science, engineering, the social sciences and humanities, and more). Within computer science, HCI tends to focus on how to design and build useful technologies that solve problems and enable new possibilities, given a domain and a problem space. This involves:

• data considerations – how and why data are constructed from real-world phenomena, the strengths and limitations of particular models, and the conditions that must be satisfied in order for a model to function in a particular context.
• design considerations – understanding the problem space and user needs; applying research-informed methods and principles for designing accessible, intuitive, and useful interfaces; and exploring how emerging technologies could be applied in novel ways.

HCI also extends beyond designing and building new technologies. In fact, much of HCI involves taking stock of how computing infrastructures interact with individuals, institutions, and society more broadly – across a wide range of human experiences (relationships, mental health and wellness, politics, fairness and accountability, sustainability, and so forth). In other words, describing, reflecting, critiquing, and debating the role of computing in society is also an integral part of the HCI discipline.

About This Course

In this course (MSAI Edition), we will focus on HCI principles as they relate to three different AI-related roles:

• The data practitioner – someone who thinks about how data resources are designed and utilized to support critical user interactions.
• The interface designer – someone who considers how user interactions ought to be designed to support various use cases, so that the system has value to the people who use it.
• The manager – someone who thinks about the “big picture” of a data-intensive system, including how all of the pieces fit together, the value propositions offered by the system, and the economic, social, and ethical dimensions of the system architecture.

Tentative Schedule

As the roles above suggest, this course is divided into three units – data, user interfaces, and special topics and applications in HCI/AI. Because this is a new version of CS330, intended to be tailored to MSAI students, many of these lectures, readings, and labs are subject to change based on your experiences and feedback. It is your responsibility to keep up with the course and any schedule changes.

Unit 1. Data

In the data unit, we will focus on some of the human aspects of working with data: how to identify and represent resources as data, and how data resources support particular kinds of interactions. We will also draw on some ideas from critical data studies to think about the biases and unintended consequences of representing the world as data in particular ways.

Unit 2. User Interfaces

In the interfaces unit, we will explore some “classic” interface topics by considering how to design usable, visually pleasing, accessible, and useful interfaces. This will involve learning some design principles and methods, and also learning some basics of “front-end” technologies:

Unit 3. Special Topics & Applications in HCI/AI

Finally, in the special topics unit, we will explore a series of questions, which include (but are not limited to):

• Given that big data, machine learning, and AI have already had a huge impact in the world, how do we think about these impacts in terms of benefits, harms, and potential risks?
• How can we design for productive AI-human collaborations?
• How should we think about trust and accountability in ML and AI systems?
• How do recommendation systems work, and what can go wrong?

To answer these questions, we will read and discuss research papers and industry projects related to the following topics (this list is subject to revision):

• Explainable / Interpretable ML
• Recommendation systems
• Continual learning models
• “Mixed Initiative” Human Systems
• Human Computation & Crowdsourcing
• Privacy
• Fairness, bias, ethics, & power

Course Format

1. Team Project

The heart of this course is a quarter-long team project. You will work in teams to design and test a novel user interface around and idea of your team’s choosing.

2. Individual Homework Assignments

You will also work on individual homework assignments throughout the quarter. These assignments will vary – some will be coding assignments to get you familiar with different technologies and design methods. Some will be written assignments that will ask you to analyze existing systems or examine a series of case studies. All of these assignments are intended to help you develop your thinking about design decisions and their tradeoffs.

3. Lectures & Discussions

Lectures and discussions will take place every Monday and Wednesday online. We will use classroom discussion as a form of collaborative sensemaking to understand, critique, and interrogate the readings and principles we are learning about. Your timely and engaged attendance at every class is thus very important – both for you and for your classmates.

4. Weekly Labs

On Fridays, you will be completing labs. Some of the labs will help you install, configure, and build a ‘full stack web application.’ Other labs will involve designing, building, and testing versions of your final project. Lab attendance is required, but you may miss one lab without a grade penalty.

5. Readings

I have selected course readings to deepen and expand your understanding of HCI fundamentals beyond what we cover in lecture. Readings should be done before class on the day they are assigned, and you will be expected to apply the principles and ideas from the readings to your homework and final project prototypes.

Expectations

• You will come to class prepared and ready to engage in an intellectual discussion about the readings and labs.
• You will complete all assignments on time and with interest, engagement, and intellectual curiosity.
• You will be a strong contributor to your team when doing group work.
• You will bring your unique expertise, perspectives, and experiences to class and share them with others, so that we might all gain from your perspectives.
• You will respect and seek to understand the unique perspectives and experiences of others.
• You will give your classmates the benefit of the doubt (about their competence and intentions) and can expect the same from them.
• All work that you submit will be your own original work; you will cite others’ work where appropriate.

Grading

Your final grade will be assessed as follows:

40% – Homework

You will have between 4 and 5 homework assignments that will each be worth roughly 10% of your grade.

40% – Team Project Work

You will also have a series of 5-6 project deliverables relating to your final project that will each be worth roughly 5-10% of your grade (depending on the deliverable).

20% – Participation

Your participation grade will be based on:

• Evidence that you have completed the readings, as reflected through your in-class participation.
• Your engagement during labs, which includes the quality of your lab submissions and your lab attendance. You may miss one lab without a grade penalty.

Other Grading Logistics

Final grades are assigned on a fixed scale: 93-100 is an A, 90-92.99 is an A-, 87-89.99 is a B+, etc. Some other logistics:

1. Final course grades will not be rounded or curved.
2. We will not be using the final exam time for this class.

Getting Questions Answered

• Office Hours: Friday 2-3PM (after lab)
• We love questions and we hope that you will feel comfortable asking questions about the readings, homework, labs, projects, or anything else related to the course.

A Note on Class Recordings

The Provost’s office has asked instructors to publish the following language in the syllabus:

Class Recordings

This class or portions of this class may be recorded by the instructor for educational purposes. These recordings will be shared only with students enrolled in the course and will be deleted at the end of the Spring Quarter, 2020 course. Your instructor will communicate how you can access the recordings.

Policy On Unauthorized Student Recording of Classroom or other Academic Activities

Unauthorized student recording of classroom or other academic activities (including advising sessions or office hours) is prohibited. Unauthorized recording is unethical and may also be a violation of University policy and state law. Students requesting the use of assistive technology as an accommodation should contact AccessibleNU. Unauthorized use of classroom recordings — including distributing or posting them — is also prohibited.

Under the University’s Copyright Policy, faculty own the copyright to instructional materials — including those resources created specifically for the purposes of instruction, such as syllabi, lectures and lecture notes, and presentations. Students cannot copy, reproduce, display or distribute these materials. Students who engage in unauthorized recording, unauthorized use of a recording or unauthorized distribution of instructional materials will be referred to the appropriate University office for follow-up.

Accommodations for Students with Disabilities

Any student requesting accommodations related to a disability or other condition is required to register with AccessibleNU (847-467-5530) and provide professors with an accommodation notification from AccessibleNU, preferably within the first two weeks of class. All information will remain confidential.