Instructor
Ming Yin
Office: LWSN 2142B
Email: mingyin [AT] purdue.edu
Office Hours: By Appointment
Teaching Assistant
Amy Rechkemmer
Course Description
Human-centered computing sits at the intersection of computer science, economics, psychology, management science, and other social sciences. It concerns developing empirical understandings and designing computational systems and techniques to enable effective interactions between people and machines for solving complex problems. This course surveys the state of the art in this area. Topics of interests include incentive design, workflow design, quality control and intelligent management in crowdsourcing, computer supported collaborative work, and design of fair, accountable, transparent, explainable, trustworthy intelligence systems to enhance human-AI interaction.
Course Schedule
See the calendar page.
Grading
- Assignment: 10%
- Reading responses: 15%
- Class discussion: 10%
- Paper presentation: 20%
- Final project: 45% (proposal+pitch: 5%, midterm report: 10%, final presentation: 15%, final report: 15%)
Paper Reading, Presentation and Discussion
Most classes in this course consist of paper reading, presentation and discussion. Specifically, in a typical class, we will cover 1-2 papers on one topic, and 1-2 students will be assigned to give a presentation on this topic. Responsibility of presenters of one class include:
- Read all required paper(s), and at least one optional paper, for that class.
- After discussing with the instructor (one week before the class), post 2-3 conversation-provoking questions related to the required paper(s) of that class.
- Give a presentation in class, which should review all required paper(s) of that class, and also briefly introduce the optional paper(s) that they have read.
- Lead the discussion in class.
Responsibility of non-presenters of one class include:
- Read all required paper(s) for that class.
- Before class, provide reading responses to all questions that the presenters of that class post.
- Participate in the discussion in class.
Final Project
Final project serves as an opportunity for students to get hands-on experience in human-centered computing research. Projects are open-ended; sample projects include:
- Design and develop crowdsourcing workflows, platforms or systems for innovative use cases.
- Design and conduct online experiments to understand the behavior of crowd workers.
- Propose new methods to improve the efficiency of crowdsourcing processes.
- Understand dynamics in current crowdsourcing or social computing systems through theoretical or empirical data analysis.
- Design and conduct online experiments to investigate how various factors of AI systems affect trust and adoption of the systems.
- Construct new interpretable ML / fair ML methods (e.g., for innovative use cases / types of data) and compare its effectiveness with existing methods through user studies.
Students are also encouraged to connect the final project with their own research.
Students can complete the project either individually or in a group of two. Tasks related to the final project include:
- Submit a project proposal which identifies the problem that the project aims to solve.
- Give a pitch presentation on the project proposal in class.
- Submit a mid-term report on the project progress and get feedback.
- Give a final presentation on the project in class, reporting the results of the project.
- Submit a final project report summarizing the project.
More detailed instruction on the final project will be provided through project guidelines.
Prerequisites
Basic programming skills required. Students should be comfortable with at least one programming language (e.g., C, Java, Python, etc.). Knowledge with artificial intelligence and machine learning is welcome.
Required Texts
No textbook is required for this course.