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Extensive HCI research has investigated how to prevent and mitigate harassment in virtual spaces, particularly by leveraging human-based and Artificial Intelligence (AI)-based moderation. However, social Virtual Reality (VR) constitutes a novel social space that faces both intensified harassment challenges and a lack of consensus on how moderation should be approached to address such harassment. Drawing on 39 interviews with social VR users with diverse backgrounds, we investigate the perceived opportunities and limitations for leveraging AI-based moderation to address emergent harassment in social VR, and how future AI moderators can be designed to enhance such opportunities and address limitations. We provide the first empirical investigation into re-envisioning AI’s new roles in innovating content moderation approaches to better combat harassment in social VR. We also highlight important principles for designing future AI-based moderation incorporating user-human-AI collaboration to achieve safer and more nuanced online spaces. 

Computer-mediated collaboration has long been a core research interest in CSCW and HCI. As online social spaces continue to evolve towards more immersive and higher fidelity experiences, more research is still needed to investigate how emerging novel technology may foster and support new and more nuanced forms and experiences of collaboration in virtual environments. Using 30 interviews, this paper focuses on what people may collaborate on and how they collaborate in social Virtual Reality (VR). We broaden current studies on computer-mediated collaboration by highlighting the importance of embodiment for co-presence and communication, replicating offline collaborative activities, and supporting the seamless interplay of work, play, and mundane experiences in everyday lives for experiencing and conceptualizing collaboration in emerging virtual environments. We also propose potential design implications that could further support everyday collaborative activities in social VR 

Objective This work examines two human–autonomy team (HAT) training approaches that target communication and trust calibration to improve team effectiveness under degraded conditions. Background Human–autonomy teaming presents challenges to teamwork, some of which may be addressed through training. Factors vital to HAT performance include communication and calibrated trust. Method Thirty teams of three, including one confederate acting as an autonomous agent, received either entrainment-based coordination training, trust calibration training, or control training before executing a series of missions operating a simulated remotely piloted aircraft. Automation and autonomy failures simulating degraded conditions were injected during missions, and measures of team communication, trust, and task efficiency were collected. Results Teams receiving coordination training had higher communication anticipation ratios, took photos of targets faster, and overcame more autonomy failures. Although autonomy failures were introduced in all conditions, teams receiving the calibration training reported that their overall trust in the agent was more robust over time. However, they did not perform better than the control condition. Conclusions Training based on entrainment of communications, wherein introduction of timely information exchange through one team member has lasting effects throughout the team, was positively associated with improvements in HAT communications and performance under degraded conditions. Training that emphasized the shortcomings of the autonomous agent appeared to calibrate expectations and maintain trust. Applications Team training that includes an autonomous agent that models effective information exchange may positively impact team communication and coordination. Training that emphasizes the limitations of an autonomous agent may help calibrate trust.