# XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
## 4 Human-Centred Evaluation Methods
### Understandability  
Understandability can be defined as \"the ability to characterize the relation between the input and output of a system with respect to its parameters\" [26]. For a system to be understandable, one could say that it must \"support user understanding of system's underlying functions\" [3]. In the cognitive psychological field, a user's understanding is usually defined as the user's mental model. Researchers in HCI analyze these users' mental models to evaluate the level of understanding of a system, a method that can be applied to ML systems. In the case of XAI systems, users come across explanations that help them build a mental model of how the system works, supposedly more accurately than in a non-XAI version of the same system. Therefore, studying these users' mental models in XAI systems is a method of assessing the effectiveness of explanations in increasing Understandability. Explanations and their relationship with understanding have been studied for several years in human-AI interaction research, focusing mainly on figuring out what are the important characteristics of an ideal explanation [27]. Firstly, user understanding could be assessed through user expectations, which would also tell researchers what type of explanations should be generated. [PERSON] and [PERSON] [28] investigated user understanding of algorithmic curation in Facebook's News Feed, and whether they believed that all their friends' posts appeared on their feed. The results showed a wide range of beliefs and causal inferences which were highly dependent on the personal experience of each user. Consequently, these perception differences also affect how the user interacts with the system. [PERSON] and [PERSON] [29] conducted two experiments to understand what information users are interested in when interacting with a few real-world applications. The authors defined a set of types of explanations the user might want, and tried to define if that information would satisfy the user's need. As mentioned in the User Trust section, [PERSON] et al. [20] stated that \"understanding of processing logic is more important for user Trust than the history of observed results alone\". This work assessed **User's Perceived Understandability** through an image classification review task. The experiments showed a significant difference between strong and weak explanations, where the tendency was to have lower user Trust when the subjects did not understand the explanation. Without human-meaningful explanations, people expected the system to fail more based on past observations. [PERSON] et al. [30] also evaluated the perceived Understandability using transparency explanations and justifications on a system with \"unexpected negative events\". The results support the authors' hypothesis that explanations can help reduce the user's Trust loss when he faces one of those events, in comparison to an incomprehensible system that does not produce justifications for them. **User Prediction of Model Output** is connected to the user expectations of a model and can be used to evaluate the level of Understandability of a model. [PERSON] et al. [31] applied a set of evaluation methods to their proposed XAI methods, LIME and SP-LIME, where one of them was focused on evaluating if the generated explanations lead users to relevant system insights. The authors intentionally trained a bad classification model, and subjects had to answer a couple questions regarding their understanding of the model output and what were the relevant features used by the model in that scenario. Results suggested that explanations were very useful in providing model insights and in deciding when not to trust the model and why. In a follow-up work [32], the same authors proposed another XAI method, Anchors, which represent local and \"sufficient\" conditions for a model's prediction. The user study compared these explanations with linear explanations on the same classification model and evaluated the ability of users to accurately predict the model output.