SWEET Lab

Human-AI Relationships and Communication

This project explores the motivations, processes, and outcomes of engagement with AI chatbots, with a particular focus on self-disclosure, trust, and relationship development. Our studies will contribute to the theory-building of human-machine communication and inform the design of social chatbots that are empowering and beneficial to users. Through this exploration, we seek to establish guidelines and best practices to foster meaningful, beneficial, and harmonious relationships between humans and machines, shaping a future where technology truly complements the human experience.

Selected publications: (Lab members are bolded)
1. Li, H., & Zhang, R. (2024). Finding Love in Algorithms: Deciphering the Emotional Contexts of Close Encounters with AI Chatbots. Journal of Computer-Mediated Communication, 29(5), zmae015. [link]
2. Ng, S.W.T., & Zhang, R. (2025). Trust in AI-driven chatbots: A systematic review. Telematics and Informatics, 102240. [link]
3. Zhu, Z., Tan, Y., Yamashita, H., Lee, Y.C., & Zhang, R. (2025). The Benefits of Prosociality towards AI Agents: Examining the Effects of Helping AI Agents on Human Well-Being. Proceedings of the 2025 ACM CHI Conference on Human Factors in Computing Systems. [link]

Responsible AI and AI Ethics

This line of work examines the socio-emotional harms of AI systems—harms that are often implicit, context-dependent, and gradual. We study how these harms emerge, evolve, and shape people’s emotions, relationships, and well-being over time. By developing deeper theoretical and empirical insights into how AI influences users on a relational and emotional level, our goal is to inform the responsible and human-centered design, evaluation, and governance of AI systems, especially those that interact with people in intimate or emotional contexts.

Selected publications:
1. Zhang, R., et al. (2025). The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships. Proceedings of the 2025 ACM CHI Conference on Human Factors in Computing Systems. [link]
2. Zhang, R., Li, H., Chen, A., Liu, Z., & Lee, Y.C. (2024). AI Privacy in Context: A Comparative Study of Public and Institutional Discourse on Conversational AI Privacy in the US and China. Social Media + Society. [link]
3. Luo, X., & Zhang, R. (2024). Decoding the gendered design and (dis) affordances of face-editing technologies in China. International Journal of Human-Computer Studies, 181, 103149. [link]