Humam-AI interaction

– Overview –

My recent research focuses on enhancing explainability and transparency in AI applications to build users’ trust, particularly in scenarios with biases. I also explore ways to measure and improve users’ AI literacy, enabling them to better understand the technology they use and feel confident in making informed decisions.

– Publication –

  • J31. Yuan, C.W.*, Tsai, H. S., & Chen, Y.-T. (2024). Charting competence: A holistic scale for measuring proficiency in artificial intelligence literacy. Journal of Educational Computing Research.
  • J30. Hou, T.-Y., Tseng, Y.-C., & Yuan, C.W.* (2024). Is this AI sexist? The effects of a biased AI’s anthropomorphic appearance and explainability on users’ bias perceptions and trust. International Journal of Information Management.
  • P13. Yuan, C. W.*, Bi, N., Lin, Y.-F., Tseng, Y.-H. (2023). Contextualizing user perceptions about biases for human-centered explainable artificial intelligence. Proceedings of the annual ACM Conference on Human Factors in Computing Systems (CHI 2022, acceptance rate 28.39%).