## 1 Introduction
In the present work, we address this gap by investigating the communication from the AI to the user in potentially erroneous scenarios and its influences on the efficiency of User-AI collaboration. By error communication, we mean the level of detail in the system's introduction to the user before its deployment, specifically, the elaboration on the AI accuracy. The risk factor is addressed in terms of decision consequences. To address this knowledge gap, we evaluate whether the user would be less willing to follow the model's suggestion once aware of the possible errors, even given an overall high accuracy of the AI, or, conversely, whether it would benefit the user's performance. Consequently, we address whether conveying the potential AI error to the user will reduce the time necessary for them to settle on either actively intervening in the model's decision or going along with the model's prediction. In the present study, the latter scenario is regarded as a more efficient User-AI collaboration. Notably, the vast majority of existing research on AI's error communication is focused on models from rather remote modalities, such as making decisions on speed dating results [30] or predicting a person's yearly income based on their demographics [31]. Additionally, one problem of the mentioned studies is that users typically perform as well as the proposed AI model or worse. This is not applicable in scenarios with assistive technology as the user often precisely knows their intention, be it picking a specific object using a robotic arm or navigating the room using an AI-driven wheelchair. To improve the ecological validity, we designed a paradigm emulating a realistic scenario for a user intending to grasp an object. We implemented the experiment in virtual reality (VR) to maintain a high level of realism and increase the participant's immersion in the environment [32]. The study is meant to be an initial step within a larger research project aiming to develop an AI-supported system to assist users with motor impairments in daily tasks such as grasping an object using a prosthetic arm. Thus, VR is an essential tool for the experimental design enabling further applicability of the findings. We conducted a VR study in which the participants were introduced to an AI model that predicted the object of interest the participant intended to grasp. Before confirming each final decision, the participants had to either check the AI's decision or accept it without intervening. Checking the AI's decision allowed the user to correct it. To engage the participants in the paradigm, we implemented a scoring system that allows participants to collect points if the final decision on the object was right or lose points when the decision was false. Critically, users had to invest certain points in checking the AI's decision. With such a scoring system, we enable the user to collaborate with the AI model. The goal of the task was to maximize the number of points. To incorporate the potential error of AI systems, we simulated two scenarios where the AI performed at different levels of accuracy as suggested by prior work [31]. A low-predictive-power (low-accuracy) model represents a high-potential-error AI. Another high-predictive-power (high-accuracy) model represents a low-potential-error AI. The key approach of the study was to compare the participants' behaviors when the potential error of the AI was not disclosed and when it was revealed to the participants. In doing so, we investigate the influence of communicating to the user the potential AI errors on the user's performance. Additionally, we introduced two levels of risk in terms of decision consequences by varying the number of points participants can lose on incorrect final decisions. This variation allowed us to gain insights into the impact of risk communication on user performance. The present study provides insights for AI developers in various sectors, particularly assistive technology.