Nur Yildirim
University of Virginia
yildirim@cmu.edu
Bio
Nur Yildirim is an assistant professor of data science at the University of Virginia. Her research focuses on bringing design thinking and participatory approaches to AI product development, specifically to engage domain stakeholders in early-phase innovation. Nur worked at Google Research and Microsoft Research on human-centered AI innovation. She is the recipient of a Digital Health fellowship from the Center for Machine Learning and Health and was named an AI Rising Star by Michigan AI Lab. The National Institutes of Health, the National Science Foundation, and Accenture have supported her work. She holds a Ph.D. in Human-Computer Interaction from Carnegie Mellon University. Before Carnegie Mellon, she worked as a design consultant in the industry, shipping award-winning products ranging from medical to consumer electronics, assistive robots, and toys.
Areas of Research
- Human-Computer Interaction
Closing the AI Innovation Gap
Advances in Artificial Intelligence (AI) have enabled a myriad of unprecedented technical capabilities Ð a collection of technologies with output uncertainty that we broadly refer to as AI. Leveraging these capabilities to build and deploy applications has never been easier. While building AI systems is getting easier, situating them in the real world in ways that are beneficial for humans remains extremely challenging. Today, the majority of AI initiatives fail, often because innovation teams do a poor job of envisioning and selecting concepts to develop. They choose high-risk projects that may or may not be valuable and overlook low-risk, high-value opportunities. As AI capabilities become readily available and commoditized, critical questions arise: What should we build with AI? How do we effectively identify use cases? I argue that this breakdown stems from the current innovation process for AI åÐ a place where human-centered and participatory approaches can play a critical role in successfully transitioning these technologies into everyday life. My research goal is to support cross-functional teams discover the right things to build with AI. I develop resources and processes to help teams in ideation, selection and formulation of AI use cases. These resources sensitize teams to AIåÕs capabilities and limitations, so that team members and stakeholders can effectively collaborate and engage in the conceptualization and development of AI technologies, regardless of their background (e.g., product manager, UX designer, domain expert, others). This approach grounds AI innovation in existing AI capabilities and actual needs. It enables teams to systematically explore the problem/solution space to identify low-risk, high-value concepts before selecting what to build.