People's trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling. However, to what extent and why domain expertise impacts people's trust is not entirely clear. In addition, accurately measuring people's trust remains challenging. We share our results and experiences of an exploratory pilot study in which four people experienced with predictive modelling systematically explore a visual analytics system with an unknown prediction model. Through a mixed-methods approach involving Likert-type questions and a semi-structured interview, we investigate how people's trust evolves during their exploration, and we distil six themes that affect their trust in the prediction model. Our results underline the multi-faceted nature of trust, and suggest that domain expertise alone cannot fully predict people's trust perceptions.
What Shapes User Trust in ChatGPT? A Mixed-Methods Study of User Attributes, Trust Dimensions, Task Context, and Societal Perceptions among University Students
National Institute of Technology, Durgapur, India（印度达格apur国立理工学院）；University of Otago, New Zealand（新西兰奥塔哥大学）；IRIT, CNRS, Toulouse University, France（法国图卢兹大学IRIT与CNRS）
Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making
Microsoft Research（微软研究院）；University of Virginia（弗吉尼亚大学）；University of Washington（华盛顿大学）；University of Michigan（密歇根大学）；Human Computation Institute（人类计算研究所）；LMU Munich（慕尼黑大学）；Western Washington University（西华盛顿大学）
