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HCII Research at CHI 2019


2019 CHI Logo - weaving the threads of CHI

Thousands of the world’s top researchers, scientists, and designers are traveling to the ACM CHI Conference on Human Factors in Computing Systems (also known as CHI) this weekend. The premier international conference of Human-Computer Interaction will take place in Glasgow, UK from May 4-9, 2019.  

Human-Computer Interaction Institute researchers received two Best Paper awards and six Honorable Mention awards. Carnegie Mellon University’s most prolific writers this year were HCI Assistant Professor Lining Yao with 5 accepted papers and HCI PhD student Gierad Laput with 4 accepted papers. 

Carnegie Mellon University authors contributed to a total of 37 accepted papers, listed below. The only organization with more accepted papers than CMU in 2019 was the University of Washington.


Best Paper Best Paper Award  

“'Occupational Therapy is Making': Design Iteration and Digital Fabrication in Occupational Therapy"   -   Best Paper Award

Megan Hofmann (Carnegie Mellon University, Pittsburgh, PA, USA)
Kristin Williams (Carnegie Mellon University, Pittsburgh, PA, USA)
Toni Kaplan (Carnegie Mellon University, Pittsburgh, PA, USA)
Stephanie Valencia (Carnegie Mellon University, Pittsburgh, PA, USA)
Gabriella Han (Carnegie Mellon University, Pittsburgh, PA, USA)
Scott E. Hudson (Carnegie Mellon University, Pittsburgh, PA, USA)
Jennifer Mankoff (University of Washington, Seattle, WA, USA)
Patrick Carrington (Carnegie Mellon University, Pittsburgh, PA, USA)

Consumer-fabrication technologies potentially improve the effectiveness and adoption of assistive technology (AT) by engaging AT users in AT creation. However, little is known about the role of clinicians in this revolution. We investigate clinical AT fabrication by working as expert fabricators for clinicians over a four-month period. We observed and co-designed AT with four occupational Therapists at two clinics: a free clinic for uninsured clients, and a Veteran's Affairs Hospital. We find that existing fabrication processes, particularly with respect to rapid prototyping, do not align with clinical practice and its {do-no-harm} ethos. We recommend software solutions that would integrate into client care by: amplifying clinicians' expertise, revealing appropriate fabrication opportunities, and supporting adaptable fabrication.

"Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes"  -   Best Paper Award

Qian Yang, Carnegie Mellon University
Aaron Steinfeld, Carnegie Mellon University
John Zimmerman, Carnegie Mellon University

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice.  Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians’ decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of Unremarkable Computing, that by augmenting the users’ routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkable-ness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience.