Mixed-Initiative Interaction with Computational Generative Systems

Machine learning models provide functions to transform and generate image and text data. This promises powerful applications but it remains unclear how users can interact with these models. With my research, I focus on designing, implementing, and evaluating functional prototypes for understanding human-AI interactions. Methodologically, I focus on web-based experiments with a mixed-methods approach. Furthermore, I use these prototypes and generative models as a material to understand fundamental concepts in human-AI interactions, such as initiative, intent, and control. In an already conducted study, for example, I showed that the levels of initiative and control afforded by the UI influence perceived authorship when writing text. For the future, I plan to carry out more studies on collaborative writing. With my dissertation, I contribute to how we will build human-AI interactions and how we will collaborate with computational generative systems in future.