## 5 Evaluation Measures
### Integrity Measures
#### 5.3.1 Trust
The behavioral perspective on trust is particularly evident in the work of [PERSON] et al. [68], who argue that subjective self-reported trust may not be a reliable indicator of trusting behaviors. Instead, they measure trust by objectively assessing how often users rely on and agree with the system's decision-making. Likewise, [PERSON] et al. [69], [PERSON] et al. [53], and [PERSON] et al. [52] measure trust behaviorally by examining how often users agree with the system's decisions. [PERSON] et al. [49] and [PERSON] et al. [52] further stress that a trust metric must capture cases of over-trust and under-trust using objective measures. This involves evaluating the number of instances where humans agree with decisions wrongly made by the system (over-trust) and, conversely, where humans disagree with decisions correctly made by the system (under-trust). [PERSON] et al. [66] and [PERSON] et al. [35] also link trust to user actions but use subjective measures in their operationalization. [PERSON] et al. [35], for instance, stress that, at a minimum, a trust scale should include both a perceptual and a behavioral component, operationalized by the items _\"Do you trust the machine's outputs?\"_ (trust) and _\"Would you follow the machine's advice?\"_ (reliance) [35]. In our sample, some studies adopt trust scales, which come from service science, where trust is crucial for establishing and maintaining long-term relationships between service providers and clients. For instance, [PERSON] et al. [48] adopted the organizational trust scale from [PERSON] et al. [129], while [PERSON] et al. [58] and [PERSON] et al. [56] adopted the trust scale from [PERSON] and [PERSON] [141]. Both scales are based on self-reports and are multi-dimensional, addressing integrity, benevolence, and competence in service provision. The studies in our sample adopt these dimensions in relation to the particular context, including items to measure the perceived competence (e.g., _\"the system seems capable\"_[48], _\"the system has the expertise to understand my needs and preferences\"_[58], _\"[the system] has the expertise (knowledge) to estimate my [needs]\"_[48]), the perceived benevolence (e.g., _\"the system seems benevolent\"_[48], _\"the system keeps my interests in mind\"_[58], _\"[the system] prioritizes that I improve [myself]\"_[56]), and the perceived integrity (_\"the system is honest\"_[58], _\"[the system] makes integrous recommendations]\"_[48]). The technology-oriented operationalizations in our sample address additional technical attributes that are relevant for building trust. Adopting established technology-trust scales [137], [PERSON] et al. [75], and [PERSON] et al. [67], for instance, include self-reporting statements, which address the attributes of consistency and predictability. In their questionnaires, [PERSON] et al. [58] use a positive-polarity item, _\"The system responds the same way under the same conditions at different times\"_, while [PERSON] et al. [67] include a corresponding but negative-polarity item _\"The system behaves in an underhanded manner\"_, to measure trust. In her operationalization, [PERSON] [37] also emphasizes the role of consistency in evaluating trustability, asserting that explanations should be coherent and stable to avoid confusing users and undermining their trust. From this stance, her trustability questionnaire includes the item _\"The explanation seems consistent\"_[37]. [PERSON] et al. [75] also consider perceived understandability as an essential indicator of trust, operationalized by items such as _\"Although I may not know exactly how the system works, I know how to use it to make decisions about the problem\"_[75]. Similarly, [PERSON] et al. [49] stressed that understanding can be a proxy used to measure trust. In this regard, they instead measured trust directly, but they decided to measure constructs like understanding and persuasion instead.