SIGCHI Outstanding Dissertation Award: Profiling Artificial Intelligence as a Material for User Experience Design
Qian Yang · 2021
HCI has become especially interested in the promises and challenges of user experience of AI, such as user acceptance, human-agent teamwork, and accessibility. Less discussed, however, is that the field of HCI routinely grapples with such challenges; across the various technologies commonly referred to as AI (e.g., predictive modeling, computer vision, NLP), what shared characteristics made human-AI interaction appeared uniquely difficult to design in the first place? Synthesizing my hands-on design and research over the past six years, in my dissertation, I worked to articulate whether, why, and how human-AI interaction appears uniquely challenging to design with established HCI methods. In this extended abstract, I first describe a human-AI interaction design framework as an answer to this question. I then discuss one critical implication of this framework: Framing data-driven AI systems as living socio-technical systems that co-evolve with their users. I analogize this reframing to the shift from desktop computing to ubiquitous computing and outline the ethnographic, design, and technological research opportunities it reveals.