論文アブストラクト： Research has shown that productivity is mediated by an individual's ability to detach from their work at the end of the day and reattach with it when they return the next day. In this paper we explore the extent to which structured dialogues, focused on individuals' work-related tasks or emotions, can help them with the detachment and reattachment processes. Our inquiry is driven with SwitchBot, a conversational bot which engages with workers at the start and end of their work day. After preliminarily validating the design of a detachment and reattachment dialogue frame-work with 108 crowdworkers, we study SwitchBot's use in-situ for 14 days with 34 information workers. We find that workers send fewer e-mails after work hours and spend a larger percentage of their first hour at work using productivity applications than they normally would when using SwitchBot. Further, we find that productivity gains were better sustained when conversations focused on work-related emotions. Our results suggest that conversational bots can be effective tools for aiding workplace detachment and reattachment and help people make successful use of their time on and off the job.
論文アブストラクト： Why is it so hard for chatbots to talk about race? This work explores how the biased contents of databases, the syntactic focus of natural language processing, and the opaque nature of deep learning algorithms cause chatbots difficulty in handling race-talk. In each of these areas, the tensions between race and chatbots create new opportunities for people and machines. By making the abstract and disparate qualities of this problem space tangible, we can develop chatbots that are more capable of handling race-talk in its many forms. Our goal is to provide the HCI community with ways to begin addressing the question, how can chatbots handle race-talk in new and improved ways?
論文アブストラクト： Text messaging-based conversational systems, popularly called chatbots, have seen massive growth lately. Recent work on evaluating chatbots has found that there exists a mismatch between the chatbot's state of understanding (also called context) and the user's perception of the chatbot's understanding. Users found it difficult to use chatbots for complex tasks as the users were uncertain of the chatbots' intelligence level and contextual state. In this work, we propose Convey (CONtext View), a window added to the chatbot interface, displaying the conversational context and providing interactions with the context values. We conducted a usability evaluation of Convey with 16 participants. Participants preferred using chatbot with Convey and found it to be easier to use, less mentally demanding, faster, and more intuitive compared to a default chatbot without Convey. The paper concludes with a discussion of the design implications offered by Convey.