User interface (UI) agents promise to make inaccessible or complex UIs easier
to access for blind and low-vision (BLV) users. However, current UI agents
typically perform tasks end-to-end without involving users in critical choices
or making them aware of important contextual information, thus reducing user
agency. For example, in our field study, a BLV participant asked to buy the
cheapest available sparkling water, and the agent automatically chose one from
several equally priced options, without mentioning alternative products with
different flavors or better ratings. To address this problem, we introduce
Morae, a UI agent that automatically identifies decision points during task
execution and pauses so that users can make choices. Morae uses large
multimodal models to interpret user queries alongside UI code and screenshots,
and prompt users for clarification when there is a choice to be made. In a
study over real-world web tasks with BLV participants, Morae helped users
complete more tasks and select options that better matched their preferences,
as compared to baseline agents, including OpenAI Operator. More broadly, this
work exemplifies a mixed-initiative approach in which users benefit from the
automation of UI agents while being able to express their preferences.