- Computational Social Science
- Critical Data Studies
- Data Science
- Economics and Information
- Education Technology
- Ethics, Law and Policy
- Human-Computer Interaction
- Human-Robot Interaction
- Incentives and Computation
- Infrastructure Studies
- Interface Design and Ubiquitous Computing
- Natural Language Processing
- Network Science
- Social Computing and Computer-supported Cooperative Work
- Technology and Equity
Qian Yang is a Human-Computer Interaction (HCI) researcher and a Ph.D. candidate at the School of Computer Science at Carnegie Mellon University. Her research draws together theories and methods from design, the social sciences, and machine learning to advance human-AI interaction. She is best known for designing decision support systems that effectively aided physicians in making critical clinical decisions.
Her work has been supported by the National Institute of Health, the National Science Foundation, and the Department of Health and Human Services. During her Ph.D., she has collaborated with researchers at Google Brain, Microsoft Research, among others. She published fifteen peer-reviewed publications on the topic of human-AI interaction at premiere HCI research venues. Four of these won paper awards. She is the recipient of a Digital Health fellowship from the Center for Machine Learning and Health, a Microsoft Research Dissertation Grant, and an Innovation by Design Award from FastCompany. Her work was featured on various global media outlets. This spring she will be speaking at SXSW on designing AI products and services.
Talk: "Making AI the New Electricity: Human-AI Interaction Design in Healthcare, Language, and Beyond"
Abstract: Some claim AI is the “new electricity” due to its growing significance and ubiquity. My research investigates this vision from an HCI perspective: How can we situate this remarkable technology in ways people perceive as valuable? How could we form a symbiotic relationship between AI systems and their users, to do things neither can do on their own?
In this talk, I will discuss a number of research projects that systematically investigate these questions. Projects include the designs of clinical decision-support systems that can effectively collaborate with doctors in making life-and-death decisions and an investigation of how Natural Language Generation systems might seamlessly serve authors’ communicative intent. Each project engages stakeholders in their real-world contexts and addresses a critical challenge in transitioning AI from the research lab to the real world.
Based upon this body of work and my studies of industry practice, I propose a framework laying out the problem space of human-AI interaction design. I discuss our early work and the strategic potential in supporting effective collaboration between HCI and AI expertise.