Human-centered computing is an emerging field that aims at designing computational systems and technologies that support people to effectively interact with machines and/or other humans to solve complex problems. For example, many successful machine learning systems (e.g., the vision systems of self-driving cars) nowadays tap into the wisdom of crowd through crowdsourcing to incorporate human inputs and feedback in the loop to enhance its own intelligence. On the other hand, the increasing application of AI-based intelligent systems in assisting people in their daily activities requires a fundamental understanding of how human users understand, trust, and engage with such systems to improve their effectiveness. This course surveys the state of the art in the area of human-centered computing, with special focuses on the research on crowdsourcing (e.g., incentives, workflow design, quality control and intelligent management in crowdsourcing, computer supported collaborative work) and human-AI interaction (e.g., fairness, accountability, transparancy and explanability of AI, trust in AI, etc.).

Course Logistics

  • Time:
    Monday & Wednesday 4:30-5:45pm

  • Location:
    LWSN B134

  • Instructor:
    Ming Yin

  • Office Hour:
    By Appointment

Should I be interested?

This course should be of interests to you if you...

  • want to use the "crowd" to help with your computational tasks (e.g., label data) but don't know where to start.
  • want to improve the outcome of your crowdsourcing effort through better task design or intelligent crowd management.
  • want to explore the design of AI or machine learning systems that people can trust and make effective use of.
  • are actively/interested in doing research on human-computer interaction, artificial intelligence, machine learning, and computational social science.