## 4 Discussion
### Limitations and Future Work  
In the present work, we intended to carefully address the effect the potential error conveyed on the user's behavior. Nonetheless, it is important to comment on the existing limitations and potential future research directions. First, while the main focus of this study was the effect of disclosing the potential error on user collaboration with AI, we also expected some differences in the performance across various risk levels. It is not a trivial task to ensure that the participants perceive actual risk in the experimental setup as it is still remote from a real-life situation. In the present work, the scoring system appeared to be a reasonable approach to inducing the risk of failure. Even though no differences were found in the number of checks across various risks, the data showed a significant risk effect on the decision time and the total score. We suggest that a finer scoring arrangement could be more sensitive to the risk effect in other constructs, such as the number of checks. Future studies should systematically investigate this. Second, in the current study, the AI algorithm was only simulated. This approach ensured the stable accuracy levels of the AI system attributing to the high- and low-accuracy scenarios. However, there is the risk that the participants do not believe that there is an AI algorithm. Given the high stated accuracy of the AI system, 90%, the substantial self-reported trust ratings indicated that the participants indeed perceived there was an actual AI algorithm in the experiment. While the trust questionnaire sufficiently served our purpose in this study, future researchers should focus on the role of trust and investigate how disclosing the potential system error affects the trust of the user in AI. Third, the present study is intended to provide implications primarily for the assistive technology application. However, the experiment was performed by young healthy participants and not patients. The VR paradigm enabled the realistic simulation of a grasping task in the context of assistive technology as well as a high level of realism and a stronger immersion of the participants into the experiment. Nonetheless, future studies should closely examine to what extent the findings of the present study can be transferred to real patients. Fourth, the number of participants in the present study (\(n=15\)) is not extensive. We argue that the experiment was intended to reproduce a more realistic scenario of a grasping task in the context of assistive technologies. Thus, as previously discussed, VR technology was selected for the paradigm implementation. Collecting the data from the participants using VR is typically resource-consuming and, therefore, challenging for the set size. The results of the present study serve as an initial step in evaluating the influence of disclosing the AI potential error to the user prior to the system deployment on the efficiency of User-AI collaboration while enabling a more realistic implementation. Future studies should extend the number of the participants to strengthen and expand the findings of the present work. Finally, it is important to mention individual variability. In our experiment, some users were generally more eager to delegate their final decision to the AI than others due to personal preferences. It manifests in the number of checks being nearly zero for some participants while reaching several dozens for others. This, on one hand, creates a challenge for the researchers striving to draw generalizing conclusions. On the other hand, it opens up new opportunities to better understand users' needs and contribute to developing more efficient human-centric AI systems tailored to the user. Beyond the scope of the present work, it is compelling to deliberately study the individual variability in the error conveying effect on the user's behavior.