## 2 Explainable Artificial Intelligence (XAI): Evaluation Theory
#### 3.2.1 Scoping Review Methodology  
In our literature review, we investigate how these elements of rigorous evaluation studies have been implemented to identify best practices, common challenges, and innovative approaches in the field. For this, our review employs a scoping review methodology [20] to explore and synthesize the practices used in conducting empirical, human-centered evaluations of explainable artificial intelligence (XAI) systems. The goal of this scoping review was to encompass a broad and diverse range of studies to capture the heterogeneity of research approaches taken in this emerging and interdisciplinary field.  
We conducted comprehensive searches in academic databases, including IEEE Explore, ACM Digital Library, Scopus, and Google Scholar, using relevant keywords such as Explainable AI, user study, empirical evaluation, and evaluation metrics. Given the rapidly evolving nature of XAI research, we observed no consistent taxonomy available for systematic, keyword-based searches across studies in XAI evaluation. In response to this challenge, we adopted an open and iterative approach, identifying relevant studies through cross-referencing papers and searching the literature on topics and terms that emerged during our review process. This approach allowed us to include heterogeneous sample studies, covering a variety of research designs, target domains, and evaluation metrics.  
However, this type of scoping review has certain limitations. In contrast to systematic literature reviews [20] in well-developed research fields with established terms and taxonomies, the rigor of bibliometric analysis and keyword searches for identifying all relevant studies is limited. As a result, our scoping review may have omitted some studies because they remained undiscovered in the iterative process. Additionally, the open nature of this process may introduce a degree of selection bias, as it relies on the researcher's judgment to identify and include relevant studies. This is particularly true for studies (such as [25; 26; 27; 28]) in which the authors of this paper were personally involved. We discussed whether to include these studies in the sample or not. Ultimately, a decision was made to use the same selection criteria to include these studies only if they shed light on existing practices for planning and conducting XAI evaluation studies.  
Despite these limitations, our scoping process's open and iterative nature enabled us to capture a more comprehensive and nuanced picture of the current state of empirical evaluations in XAI. Moreover, our explorative approach shows the diversity and complexity of XAI evaluation practices, especially in terms of research design, evaluation metrics, and methodologies.  
By analyzing the papers from this stance, we could identify various patterns and categories (see Table 1). For instance, regarding research objectives, we saw methodology-driven, concept-driven, and application-driven studies as common research genres. Regarding the evaluation scope, we saw that the studies address various application domains (such as healthcare, law/justice, finance, etc.), where both highly critical real-world and less critical, illustrative scenarios had been addressed. We were also able to identify different target group types, such as end-users/affected persons, regulators/managers, and developers/engineers. Regarding measurement, we uncovered three main areas, namely understandability, usability, and integrity. By assessing implementation and procedures, our analysis reveals that using proxy users recruited by an online panel was a common pattern. Lastly, we looked at the data collection methods employed, such as observations, interviews, and surveys, and how they aligned with the study objectives and constructs being measured.  
We present our findings in detail in the following sections. This not only enhances our understanding of current methodologies but also contributes to guiding future research efforts for more effective and accurate evaluations in similar domains.