## 4 Evaluation Scope
### Test Scenarios
#### 4.3.2 Illustrative Scenarios with Less Critical Impact  
This category encompasses domains or evaluation scenarios where AI decisions have minor impacts or researchers envision simple scenarios to illustrate an approach and the explanations produced [8]. A common method used in this category is to isolate the explanation mechanism from specific contexts to better understand its fundamental properties and impacts. For example, [PERSON] et al. [61], [PERSON] et al. [75], and [PERSON] et al. [16] utilized generic image classification scenarios to evaluate various explanation methods. [PERSON] and [PERSON] [59] used the labeling of handwritten digits in a decontextualized evaluation scenario. Similarly, [PERSON] et al. [72] presented various explanation methods for text, images, audio, and sensor data in a non-contextualized manner to determine user preferences for these methods. [PERSON] et al. [61] explored the role of explanations in a decontextualized setting where participants were asked to interpret and respond to questions about charts and tables. There are also cases where no specific domains are addressed or where evaluation scenariosare not well specified. [PERSON] et al. [35] focused on theoretical criteria for evaluation studies, not on empirical research. An additional approach is to use toy examples and fictitious scenarios. To prevent confounding effects and avoid triggering everyday habits, biases, and established preferences, these scenarios are intentionally designed to be distinct from familiar environments and real-world applications. [PERSON] et al. [42], for instance, employed proxy, artificial tasks such as predicting the AI's decision-making regarding the percentage of fat content in a plate. [PERSON] et al. [51] defined an alien food preference and an alien medicine treatment scenario for their evaluation study. [PERSON] [34] and [PERSON] et al. [64] designed fictitious robot scenarios for their evaluation study. [PERSON] et al. [66] used a scenario based on the Diner's Dilemma, where several diners eat out at a restaurant and agree to split the bill equally over an unspecified number of days. All of these studies allow for the examination of explanation methods in a controlled, non-realistic task. Using fictitious application scenarios in evaluations aids in engaging participants and facilitating their understanding of the context. At the same time, detaching the evaluation from real-world scenarios comes with a trade-off that reduces the ecological validity of the results. Another approach is to adapt familiar contexts to enhance the participants' understanding and engagement with the abstract concepts being evaluated. The study by [PERSON] et al. [38] is an example of this approach, in which explanation concepts were evaluated with the help of the well-known Tic-Tac-Toe game. In a similar vein, [PERSON] et al. [39] used an art recommendation scenario, and [PERSON] et al. [48] used the widely known Quick-Draw platform for this purpose. [PERSON] et al. [60] and [PERSON] [53] utilized a sentiment labeling task for online movie reviews as a familiar context to many internet users to evaluate explanation systems. [PERSON] et al. [36] evaluated their data-centric explanatory approach using four decontextualized but familiar scenarios: predictive bail decisions, facial expression recognition, automatic approval decisions, and automatic speech recognition. Similarly, [PERSON] et al. [65] used a repeat- offender scenario for their evaluation, [PERSON] et al. [69] used the toxicity of social media posts, while [PERSON] et al. [15] used the default risk assessment scenario for credit applications. [PERSON] [28, 51], used a common but fictional online shopping scenario to evaluate explanations for finding appropriate digital cameras. Overall, the approach of using fictional and toy examples minimizes the complexity inherent in real-world settings and reduces potential confounding variables, thereby facilitating a clearer understanding of the general effects of explanatory systems.