# XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
## 4 Human-Centred Evaluation Methods
### Summary  
Table 3 summarizes the previously described state-of-the-art methods by presenting a condensed view of the research questions, evaluation methods/metrics used and the type of XAI explanation generated for each work. Each row represents a Method/Category pair, as there are situations where the same work proposes different metrics for different categories. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Category** & **Sub-Category** & **Research Question** & **Methods and/or Metrics** & **XAI Type** \\ \hline \multirow{4}{*}{[PERSON] et al., 2018 [65]} & Explanation & & What is the impact of & & **SUS questionnaire and 5-point Likert scale to evaluate Perceived & \\  & Explantion & & intelligibility on the Perceived & & Uselines, general (Kability) and the perception of the following & \\  & Usefulness and & - & Value, Trust, Usage, Performance & features: understandable, useful, epiphy, trustworthy, improves & \\  & Staffaction & & and User Satisfaction for & & quality and efficiency. & \\  & & different translation adds? & & & \\ \hline \multirow{4}{*}{[PERSON] et al., 2012 [67]} & Explanation & & To what extent does the & & **Qualitative interests, 2-week diary and 2-point Likert scale** & \\  & Usefulness and & - & participant want to know more & & & \\  & Satisfaction & & about how the system generates & & & \\  & & its intelligent behaviour? & & & & \\ \hline \multirow{2}{*}{[PERSON] et al., 2009 [142]} & Explanation & & How to evaluate if different types & & & \\  & Usefulness and & - & of explanations improve the & & their perceptions of the explanations and system in terms of & \\  & Satisfaction? & & Users’ perception of systems’ & & & \\  & & Usefulness and Satisfaction? & & & & \\ \hline \multirow{4}{*}{[PERSON] et al., 2016 [31]} & Explanation & & & Can non-operative improve a & & \\  & & classifier through explanations? & & on feature importance. & & \\ \cline{2-6}  & & & How do explanations influence & & & \\  & & use Performance when trying to & & & & \\  & & predict model behaviour on & & & & \\  & & & unseen instances? & & & \\ \hline \multirow{4}{*}{[PERSON] et al., 2018 [32]} & \multirow{4}{*}{Performance} & \multirow{4}{*}{-} & \multirow{4}{*}{-} & How updates to an AI system & & & \\  & & can affect human-AI team & & Locally-emphing update: When doing why the new & & \\  & & & Federmaner & model version affect the & & \\  & & & model version change: Globally-emphing update: Update in & & \\  & & & models for all-methods (users). & & & \\ \hline \multirow{4}{*}{[PERSON] et al., 2009 [142]} & \multirow{4}{*}{Performance} & \multirow{4}{*}{-} & How to evaluate if different types & & & \\  & & of explanations lead to better task & & & \\  & & Performance? & & & & \\ \hline \multirow{4}{*}{[PERSON] et al., 2016 [23]} & \multirow{4}{*}{Trust} & \multirow{4}{*}{Evolution of User Trust} & \multirow{4}{*}{Evolution of User Trust} & \multirow{4}{*}{How does user trust evolve over time?} & \multirow{4}{*}{**-Point Likert scale** to indicate the extent of agreement with a statement of trust in the system before and after performing a task & \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Human-centred evaluation methods for machine learning explanations.