## 1 Introduction  
In the last few decades, artificial intelligence (AI) has been swiftly integrated into numerous sectors of our day-to-day life, be it health [1], workspaces [2], education systems [3], or even our private lives [4]. AI-enhanced technologies also offer novel ways to help users to overcome various personal limitations [5], ranging from augmentations, such as enhanced vision aids [6, 7] or mobility assistance [8], up to cognitive aids to slow down the development of dementia [9] and other mental burdens [10]. Despite significant improvement in AI algorithms driven by the recent hardware and software advancements, it is still inevitable that the model may fail to meet the user's needs and expectations, similar to other technologies [11]. Even highly accurate AI algorithms might be subjected to incorrect predictions. As AI technologies are fundamentally data-driven models, their performance highly depends on the training data set used to train the models. Next to biases in the data that influence the predictive power, a training set is unlikely to cover the full range of variance present in the data of the population. Thus, the error potential of AI algorithms is, at least to some extent, always present and therefore needs to be considered. In the context of assistive technology, AI models can furthermore be challenged by the limited data quality on which inference is based. For example, if the patient's capacity is impaired, intention prediction might not reflect the user's natural behavior, resulting in undesired consequences for the patient [12]. There are several factors that may affect the user's acceptance of such technologies, such as the performance of the system. Witnessing an AI producing errors may cause users to stop using a piece of technology due to the mismatch between the AI's performance and expectations [13, 14, 15, 16, 17]. The failure to meet the user's expectations may also induce algorithm aversion, which describes the user's tendency to reject the suggestions of an algorithm once they observe its incorrect prediction, even if the model generally outperforms humans [18]. Despite the role of the system's performance on user acceptance, the usage of more sensors and complex AI-based approaches may also affect the user's acceptance [19], especially in elderly populations [20; 21]. Within the context of health and caregiving, the lack of acceptance becomes particularly acute because users may be highly dependent on the AI's performance [5; 22]. To overcome these challenges of complex AI technologies, prior work suggests ensuring the AI's model is more explainable and being transparent about the AI's reasoning and performance to the user [23], which is a core component of the concept of explainable AI (XAI) [24]. One specific goal of XAI is to strengthen users' trust in the technology and, thus, strengthen the relationship between the user and AI algorithms to help them to unlock their full potential [25]. Furthermore, the increased transparency about the AI and the underlying technology may help designers to manage the user's expectations of the capabilities of an AI. Besides transparency, the communication of risks [26] is crucial for balancing the user's expectations. Prior work emphasizes that risk perception is based on a mental model of the current situation based on direct inputs (e.g., own experience of interacting with a device) and indirect ones (e.g., reading articles about the performance of a device) [27]. Risk communication may alter the personal mental model and trust in the technology itself [28]. In AI-based assistive technologies, researchers emphasize that proper risk and error communication may become essential to help patients and medical experts adopt these technologies [29]. However, while previous research discusses the role of error and risk in separate works, the overall influence of both these factors on the user's performance is still unclear.