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AI systems are breaking into new domains and applications, and it is pivotal to center humans in contemporary AI systems and contemplate what this means. This discussion considers three perspectives or human roles in AI as users, contributors, and researchers-in-training, to illustrate this notion. 

We provide an experience report about a remote framework for early undergraduate research experiences, which was thematically focused on sensing humans computationally. The framework included three complementary components. First, students experienced a team-based research cycle online, spanning formulating research questions, conducting literature review, performing fully remote human subject data collection experiments and data processing, analyzing and making inference over acquired data with computational experimentation, and disseminating findings. Second, the virtual program offered a set of professional development activities targeted to developing skills and knowledge for graduate school and research career trajectories. Third, it offered interactional and cohort-networking programming for community-building. We discuss not only the unique challenges of the virtual format and the steps put in place to address them but also the opportunities that being online afforded to innovate undergraduate research training remotely. We evaluate the remote training intervention through the organizing team’s post-program reflection and the students’ perceptions conveyed in exit interviews and a mid-program focus group. In addition to outlining lessons learned about more or less successful framework elements, we offer recommendations for applying the framework at other institutions as well as how to transfer activities to in-person formats. 

We explore the extent to which empathetic reactions are elicited when subjects view 3D motion-capture driven avatar faces compared to viewing human faces. Through a remote study, we captured subjects' facial reactions when viewing avatar and humans faces, and elicited self reported feedback regarding empathy. Avatar faces varied by gender and realism. Results show no sign of facial mimicry; only mimicking of slight facial movements with no solid consistency. Participants tended to empathize with avatars when they could adequately identify the stimulus' emotion. As avatar realism increased, it negatively impacted the subjects' feelings towards the stimuli. 

Smiling and laughter are typically associated with amusement. If they occur under negative emotions, systems responding naively may confuse an uncomfortable smile or laugh with an amused state. We present a passive text and video elicitation task and collect spontaneous laughter and smiles in reaction to amusing and negative experiences, using standard, ubiquitous sensors (webcam and microphone), along with participant self-ratings. While we rely on a state-of-the-art smile recognizer, for laughter recognition our transfer learning architecture enhanced on modest data outperforms other models with up to 85% accuracy (F1 = 0.86), suggesting this technique as promising for improving affect models. Subsequently, we analyze and automatically predict laughter as amused vs. negative. However, contrasting with prior findings for acted data, for this spontaneously elicited dataset classifying laughter by emotional valence is not satisfactory.