My research is at the intersection of HCI and AI. I design, build and evaluate interactive systems that have some kind of machine intelligence under the hood. I strive to build intelligent interactive systems that are useful, that give people a meaningful sense of control, and whose behavior aligns with the mental models held by their users. This is challenging because the underlying AI technology can be occasionally wrong, it delivers the most value if it is allowed to act proactively, and it frequently behaves in unexpected ways. In the past two decades, the human-AI interaction community has grown and has made substantial progress in producing useful design knowledge that addresses these challenges. Machine intelligence is now present in many real-world interactive systems from nearly invisible (like predictive text helping with mobile text entry), to highly consequential (like AI-powered decision-support systems). However, there are also some important gaps in our knowledge. For example, the results of our behavioral experiments indicate that adaptive user interfaces require more cognitive effort to operate than we had assumed, predictive text changes the content of what people write instead of just making text entry more efficient, and decision makers presented with AI-generated decision recommendations and explanations rarely engage cognitively with the content of what the AI communicates. Meanwhile the results of our other studies point out some unverified assumptions underlying the common choices of what (and whose) problems we solve with AI in clinical and public sector settings.
This said, I believe that we can design useful and usable AI-powered interactive systems but the relevant design knowledge is relatively knew and is still a work in progress. The contemporary enthusiasm for using machine intelligence in interactive systems is an opportunity to grow our knowledge. It is also a danger in that it creates conditions where following the best practices of others, without having the time or opportunity to examine them, can turn unverified assumptions into fundamental principles of our field.