Conference Agenda (All times are shown in Eastern Daylight Time)

In order to help adolescents cope with loneliness during the social distancing and isolation imposed by the COVID-19 pandemic, we designed a conversational agent programmed to distract users from negative thoughts and advise them on strategies to improve their wellbeing. In order to assess the effects of the agent intervention on adolescent participants, we performed quantitative analysis of their self-reported mood states and qualitative analysis of their subjective views and opinions on the agent to help us understand their experiences. Trends in the quantitative data point to minimal changes in participants’ wellbeing and loneliness after interactions with the experimental agent. However, qualitative data on adolescent experiences suggests short and long-term positive effects of the experimental interactions. In reporting our findings, we aim to bring attention to the importance of the qualitative data for understanding human experiences with technology, as well as the limitations of the instruments developed in the field of psychology for human-information interaction research.

This study utilized a two-phase user experiment to explore people’s perceptual and cognitive states interacting with the COVID-19 dashboard to obtain outbreak information. Specifically, 27 participants were assigned to interact with this dashboard with different color arrangements and performed image-memory, search, and browse visualization tasks sequentially. We found that the participants expected to obtain both global pandemic trends and single region/date statuses from the dashboard to help them grasp important information in the shortest possible time. They also allocated their attention differently to the dashboard’s content areas to match their individual visual movement and reading logics. Our participants indicated that the pandemic data visualization dashboard should use a principal-color selection that is alarming but without causing panic. In the study’s second phase, an eye-tracking experiment, it was found that the participants’ actual eye paths deviated from our expectations: clustering around headings and text, rather than on visualized charts or graphs as anticipated. Based on these findings, we provide design implications for builders of future data-visualization and disaster dashboards.

With the advancement of science and technology, humanoid robots have been gradually adopted in human society. Given the human-like appearance, the perceived personality of a humanoid robot can affect humans’ general perceptions of the robotic agent, and even task performance. However, limited research pays attention to examining how a humanoid robot’s non-verbal cues characterize its personality traits. To solve this gap, this research aims to investigate how to use humanoid robots’ non-verbal features (textual and gestural information) to develop different kinds of personality attributes, and how these personality characteristics affect human-robot interaction. A total of 255 participants were recruited for this research, including pilot tests and two rounds of lab studies examining different levels of task complexity. The empirical results reveal the developed gestural cues allow a humanoid robot to distinguishably convey extrovert, ambivert, and introvert personality traits, where the perceived traits significantly affect user intentions to interact with the humanoid robot.

This paper discusses the role of organizational records management (ORM) with respect to the field of explainable artificial intelligence (XAI) and argues about its necessity and significance. The current trend is to utilize AI to explain AI, including both explanation production and provision. We argue that this kind of approach is not sufficient by itself as it lacks neutrality and localness to explanation recipients, thus ineffective in establishing public trust. We propose the addition of the ORM profession, as an informational 3rd party, to the current and future XAI. We envision that, by working together with XAI performers, ORM contributes to an information resilient AI society. To take on the related responsibilities, the ORM profession must improve its professional expertise and strengthen its professional independence. We call for consensus among individual information professionals and the advocacy of information professional networks to make this vision a reality.