## 2 Materials and Methods
### Analysis and Expected Behavior
#### Number of Checks  
We evaluated the difference between the total number of checks in the \"revealed\" and \"not revealed\" conditions at each risk level using a two-way repeated measures ANOVA with the condition and risk as independent variables. To further understand the participants' strategy, we inspected the difference in the number of checks between the objects corresponding to the low-accuracy scenarios and high-accuracy objects. Specifically, the effect of the condition and risk on the difference between the number of checks for  
Figure 3: Schematic representation of the experimental setup. To strengthen the immersion, a virtual chair was placed in the VR environment at the same position where the actual chair was located in the real world.  
low- and high-accuracy objects was evaluated using two-way repeated-measures ANOVA. Finally, a post hoc Tukey test was conducted.  
Although the total number of checks can provide a general idea of how often participants were willing to intervene in the model's prediction vs. delegating the decision to the AI, we primarily focused on the distribution between the number of checks for the high- and low-accuracy objects in \"not revealed\" vs. \"revealed\" conditions. The main hypothesis was that, if the communication of the erroneous scenarios to the participants positively affects the efficiency of User-AI collaboration in the \"revealed\" condition, there would be a significant shift in the number of checks toward the objects with low accuracy relative to the objects with high accuracy. It would indicate that the participants adjusted their strategy when collaborating with AI leading to more efficient performance. Alternatively, the distribution of the number of checks could stay similar among the conditions demonstrating no significant effect of the error communication. Furthermore, we expected a generally larger number of checks in the case of higher risk due to aversion to the risk of losing a lot of points.  
We inspected the total score in points for each experimental session. Generally, the scoring system was designed to regard (1) the effort of the user when collaborating with AI, (2) the undesired outcome in case of the system's failure to predict the user's intention, and (3) the benefits in the case of accurate intention prediction by the AI. Thus, the total score in the present study serves as an implicit indicator of the efficiency of the User-AI collaboration. We, therefore, examined the effect of the condition, \"revealed\" vs. \"not revealed\", and the risk on the total score using two-way repeated-measures ANOVA. Finally, a post hoc Tukey test was conducted.  
If causing participants to be aware of the low-accuracy objects positively influences the efficiency of the collaboration between the user and AI, we expected the total score to increase in the \"revealed\" condition. Alternatively, the score would stay unchanged, indicating no effect of the revealing of the erroneous scenarios. Moreover, we expected the score to be generally lower for the higher risk due to the study design, where the steps for the score increment are much larger for the higher risk.  
As an additional metric of the User-AI collaboration efficiency, we examined the time that participants needed to decide whether to continue to the next trial or to check the model's prediction. We evaluated the decision time using two-way ANOVA with the condition and risk as independent variables. Thereafter, a post hoc Tukey test was conducted.  
A more efficient User-AI collaboration would manifest in the reduction in time that participants needed to make a decision. Thus, we expected a lower decision time in the \"revealed\" condition compared to the \"not revealed\". Otherwise, no effect of the condition on decision time could be expected. Furthermore, we expected longer decision times for the high risk due to higher stakes in the case of failure.