# XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
## 4 Human-Centred Evaluation Methods
### Trust  
User Trust is a critical requirement when deploying an ML system that profoundly affects the user's perception of such system, which in turn influences the ability to believe in its outputs without fearing any danger [16]. It is inherently related to the user's confidence and to how comfortable the user is when using that system on a real task. Initially, a user's Trust is set according to prior knowledge and existing beliefs. As the user explores the system, the levels of Trust and confidence can fluctuate, depending on the use cases that the user comes across. Therefore, user Trust is a variable factor that is shaped by the user interaction, where he might experience different Trust and mistrust feelings [3]. Figure 2: Human-centred evaluation process diagram (adapted from [15]). Most scholars do not evaluate user Trust directly because it is hard to pin down what system factors influence Trust the most. Therefore, each work usually chooses a particular aspect of a XAI system and assesses its impact on user Trust. Firstly, we can evaluate user Trust through interviews and user self-explanations during or after the user experiments with the system, like in [PERSON] et al. [17] and [PERSON] [18]. Moreover, Likert-scales questionnaires are also used widely across literature, such as in [PERSON] et al. [19], [PERSON] et al. [17], [PERSON] [18] and [PERSON] et al. [20]  
Another way of assessing user Trust is through the **User's Perceived System Competence**, which shows the user position on how capable an ML system is when solving a particular task. For example, [PERSON] et al. [21] evaluated the impact of the accuracy of an image classification model on user Trust. The results show that a user's Trust can be influenced by both the actual model's accuracy and by the model's perceived accuracy. [PERSON] et al. [20] also explored how the user's perception of accuracy is affected by the inclusion of explanations and their level of meaningfulness. The results indicate that it is not only important to have explanations, but also to have human-meaningful ones, since \"weak\" explanations that the user cannot understand might lower the user perceived accuracy. Moreover, authors also state that the \"understanding of processing logic is more important for user Trust than the history of observed results alone\". Therefore, the **User's Perceived Understandability** is another important factor that influences user Trust (see Understandability Section 4.2 for more details). One could also study the **User's Compliance with the System**, which focuses on understanding if the user would rely on the system decision or not to act upon a task. [PERSON] et al. [19] evaluated the impact of several recommendation interfaces and content selection strategies on user Trust. The method considered nine key factors from Trust, divided into three categories, one of them involving explanations. [PERSON] et al. [7] experimented with \"placebic\" explanations (explanations that convey no information) in mindlessness of user behaviour. The method consisted of understanding if this type of explanation would influence user Reliance on the system. The results suggest that \"placebic\" explanations can increase the levels of Trust in a similar way to real explanations. The authors also propose that they could be used in a future work as \"baseline\" explanations that would serve as a comparison basis for other explanations. Another way of measuring any of the above aspects of user Trust is to compare explanations generated by an AI system with ones provided by humans. [PERSON] et al. [22] used this strategy on a recommender system to explore the perceived quality of explanations created by two different sources: a personal source (i.e., other humans) or an impersonal source (a machine). The results show that users tend to trust explanations created by other humans more than the explanations generated by a machine.