Junti Zhang

Peinuan Qin, Jiting Cheng, Jungup Lee et al.

Cyberbullying is a pervasive problem in online environments, causing substantial psychological harm to victims. Although bystander intervention has proven effective in mitigating its impact, motivating bystanders to engage in direct intervention remains a persistent challenge. Studies have suggested that difficulties in intervention skills and defending self-efficacy hinder bystanders from initiating direct intervention. To address this challenge, we introduced EmojiGen, an AI intervention tool designed to empower bystanders for direct intervention. EmojiGen enabled users to simply select an emoji as an intention clue, which subsequently combined the cyberbullying context to generate responses. In a between-subjects experiment involving 90 participants on a custom-built social media platform, we found that EmojiGen significantly increased the frequency of direct bystander interventions, both in supporting victims and in confronting perpetrators, driven by different factors. EmojiGen also increased the sense of knowing how to help and defending self-efficacy, while reducing perceived workload and anxiety associated with initiating intervention. The study contributed to the CSCW community through offering an effective direct bystander intervention method and providing design implications for future cyberbullying interventions.

Jingshu Li, Yitian Yang, Q. Vera Liao et al.

Complementary collaboration between humans and AI is essential for human-AI decision making. One feasible approach to achieving it involves accounting for the calibrated confidence levels of both AI and users. However, this process would likely be made more difficult by the fact that AI confidence may influence users' self-confidence and its calibration. To explore these dynamics, we conducted a randomized behavioral experiment. Our results indicate that in human-AI decision-making, users' self-confidence aligns with AI confidence and such alignment can persist even after AI ceases to be involved. This alignment then affects users' self-confidence calibration. We also found the presence of real-time correctness feedback of decisions reduced the degree of alignment. These findings suggest that users' self-confidence is not independent of AI confidence, which practitioners aiming to achieve better human-AI collaboration need to be aware of. We call for research focusing on the alignment of human cognition and behavior with AI.