## 5 Evaluation Measures
### Integrity Measures
#### 5.3.1 Trust  
Trust is a well-established concept in social science. Due to its long history and multidisciplinary nature, trust has been defined in various ways. Still, trust can generally be defined as \"_the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party_\" [129]. Concerning interactive systems, trust also refers to \"_the extent to which a user is confident in and willing to act on the recommendations, actions, and decisions of an artificially intelligent decision aid_\" [137]. It has also become an important topic in AI research, as trusting systems is essential, particularly in critical decision-making processes, when users do not fully understand how the system arrives at its conclusions or only have limited control over its behavior. This central importance is also reflected in our sample, where many studies investigate how explanations impact trust and confidence [15; 27; 28; 30; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 52; 53; 54; 56; 57; 58; 59; 60; 61; 63; 64; 65; 66; 67; 68; 69; 70; 71; 75; 76]. Analyzing the studies in our sample in detail reveals that the theoretical understanding of the concept is shaped by various disciplines such as psychology, service science, and technology studies. From a psychological perspective, trust presents a multidimensional construct encompassing both cognitive and affective components [137]. The cognitive aspect of trust refers to the user's intellectual assessment of the AI system's characteristics, such as accuracy, transparency, and reliability. On the other hand, the affective component deals with emotional responses such as feelings of safety, confidence, and comfort when interacting with the system. Institution-based trust theories complement this understanding by emphasizing trust, which is a prerequisite for a certain level of confidence in the service provider's integrity, benevolence, and competence [138]. Competence refers to the system's ability to fulfill its promises (e.g., delivering a product on time). Integrity is demonstrated by the system's consistent and reliable actions, and benevolence indicates that the system prioritizes the user's interests above its own, showing genuine concern for the user's well-being. The third perspective came from technology studies, where the conceptualization also addresses specific issues in the human-machine context, such as users perceiving a system as trustworthy when it is reliable, secure, transparent, and understandable and when the system behaves in a predictable and consistent manner [139]. In addition, there is a behavioral view in the literature on trust that emphasizes that trust is not only an internal state of mind but also becomes evident through the user's behavior--specifically when users actively engage with a system, utilize its functionalities, or follow its decision-making and recommendations [140]. In our literature survey, we observed various perspectives on trust manifesting in different ways of measuring it. Concerning the psychological dimension, various operationalizations in our sample emphasize the affective side of trust. [PERSON] et al. [15], for instance, stress that trust aims to increase the user's confidence. Similarly, [PERSON] et al. [65] Sukkerd [34], [PERSON] et al. [60], and [PERSON] et al. [43] define trust as the confident expectation that one's vulnerability will not be exploited. To measure trust, [PERSON] et al. [47] adopt the trust scale developed by [PERSON] and [PERSON] [137], which also covers the affective dimension of trust, including items like _\"I find [system] likable\"_ and _\"My interaction with [system] is enjoyable\"_. In the same vein, [PERSON] et al. [64] apply perceived likability and positive affective traits as indicators of trust in human-machine interactions.